{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Công cụ - pandas**\n",
"\n",
"*Thư viện `pandas` cung cấp các công cụ phân tích dữ liệu và cấu trúc dữ liệu hiệu suất cao, dễ sử dụng. Cấu trúc dữ liệu còn được gọi là `DataFrame`, mà bạn có thể xem như bảng 2D trong bộ nhớ (giống như bảng tính, có tên cột và nhãn hàng). Nhiều tính năng có sẵn trong Excel có được hỗ trợ trong pandas, chẳng hạn như tạo bảng tổng hợp, tính toán cột dựa trên các cột khác, vẽ đồ thị, v.v. Bạn cũng có thể nhóm các hàng theo giá trị cột hoặc nối các bảng giống như trong SQL. Pandas cũng rất mạnh mẽ trong việc xử lý chuỗi thời gian.*\n",
"\n",
"Điều kiện tiên quyết:\n",
"* NumPy – nếu bạn chưa quen với NumPy, chúng tôi khuyên bạn nên xem [Hướng dẫn về NumPy](tools_numpy.ipynb) ngay bây giờ."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" \n",
" ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\") \n",
" | \n",
" \n",
" ![](\"https://kaggle.com/static/images/open-in-kaggle.svg\") \n",
" | \n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cài đặt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Đầu tiên, hãy nạp `pandas`. Mọi người thường nạp nó dưới dạng `pd`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Đối tượng `Series`\n",
"Thư viện `pandas` chứa các cấu trúc dữ liệu hữu ích sau:\n",
"* Các đối tượng `Series` mà chúng ta sẽ thảo luận ngay bây giờ. Đối tượng `Series` là mảng 1D, tương tự như cột trong bảng tính (có tên cột và nhãn hàng).\n",
"* Các đối tượng `DataFrame`. Đây là bảng 2D, tương tự như bảng tính (có tên cột và nhãn hàng).\n",
"* Các đối tượng `Panel`. Bạn có thể xem `Panel` dưới dạng từ điển của `DataFrame`. Chúng ít được sử dụng hơn, vì vậy chúng ta sẽ không thảo luận về chúng ở đây."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tạo `Series`\n",
"Hãy bắt đầu bằng cách tạo đối tượng `Series` đầu tiên của chúng ta!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2\n",
"1 -1\n",
"2 3\n",
"3 5\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series([2,-1,3,5])\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tương tự như `ndarray` 1D\n",
"Các đối tượng `Series` hoạt động giống như các `ndarray` NumPy một chiều và bạn thường có thể chuyển chúng dưới dạng tham số cho các hàm NumPy:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 7.389056\n",
"1 0.367879\n",
"2 20.085537\n",
"3 148.413159\n",
"dtype: float64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.exp(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Các phép toán số học trên `Series` cũng có thể thực hiện được và chúng áp dụng *theo từng phần tử*, giống như đối với `ndarray`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1002\n",
"1 1999\n",
"2 3003\n",
"3 4005\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s + [1000,2000,3000,4000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tương tự như NumPy, nếu bạn thêm một số vào `Series`, thì số đó sẽ được thêm vào tất cả các mục trong `Series`. Điều này được gọi là *broadcasting*:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1002\n",
"1 999\n",
"2 1003\n",
"3 1005\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s + 1000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Điều này cũng đúng với tất cả các phép toán nhị phân như `*` hoặc `/`, và thậm chí cả các phép toán có điều kiện:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 False\n",
"3 False\n",
"dtype: bool"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s < 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nhãn chỉ mục\n",
"Mỗi mục trong đối tượng `Series` có một mã định danh duy nhất được gọi là *nhãn chỉ mục*. Theo mặc định, nó chỉ đơn giản là thứ hạng của mục trong `Series` (bắt đầu từ `0`) nhưng bạn cũng có thể đặt nhãn chỉ mục theo cách thủ công:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"alice 68\n",
"bob 83\n",
"charles 112\n",
"darwin 68\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
"s2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sau đó, bạn có thể sử dụng `Series` giống như `dict`:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"83"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2[\"bob\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bạn vẫn có thể truy cập các mục theo vị trí số nguyên, giống như trong một mảng thông thường:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"83"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Để làm rõ khi bạn đang truy cập theo nhãn hoặc theo vị trí số nguyên, bạn nên luôn sử dụng thuộc tính `loc` khi truy cập theo nhãn và thuộc tính `iloc` khi truy cập theo vị trí số nguyên:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"83"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.loc[\"bob\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"83"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.iloc[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ta cũng có thể cắt một `Series` sử dụng các nhãn chỉ mục:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"bob 83\n",
"charles 112\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.iloc[1:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Điều này có thể dẫn đến kết quả không mong muốn khi sử dụng nhãn số mặc định, vì vậy hãy cẩn thận:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1000\n",
"1 1001\n",
"2 1002\n",
"3 1003\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surprise = pd.Series([1000, 1001, 1002, 1003])\n",
"surprise"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 1002\n",
"3 1003\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surprise_slice = surprise[2:]\n",
"surprise_slice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ồ! Phần tử đầu tiên có nhãn chỉ mục `2`. Phần tử có nhãn chỉ mục `0` không có trong lát cắt:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Key error: 0\n"
]
}
],
"source": [
"try:\n",
" surprise_slice[0]\n",
"except KeyError as e:\n",
" print(\"Key error:\", e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nhưng hãy nhớ rằng bạn có thể truy cập các phần tử theo vị trí số nguyên bằng cách sử dụng thuộc tính `iloc`. Điều này minh họa một lý do khác tại sao việc sử dụng `loc` và `iloc` để truy cập các đối tượng `Series` luôn tốt hơn:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1002"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surprise_slice.iloc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Khởi tạo từ `dict`\n",
"Bạn có thể tạo đối tượng `Series` từ `dict`. Các khóa sẽ được sử dụng làm nhãn chỉ mục:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"alice 68\n",
"bob 83\n",
"colin 86\n",
"darwin 68\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
"s3 = pd.Series(weights)\n",
"s3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bạn có thể kiểm soát những thành phần nào bạn muốn đưa vào `Series` và theo thứ tự nào bằng cách chỉ định rõ ràng `chỉ mục` mong muốn:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"colin 86\n",
"alice 68\n",
"dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
"s4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Căn chỉnh tự động\n",
"Khi một thao tác liên quan đến nhiều đối tượng `Series`, `pandas` sẽ tự động căn chỉnh các mục bằng cách khớp các nhãn chỉ mục."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')\n",
"Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')\n"
]
},
{
"data": {
"text/plain": [
"alice 136.0\n",
"bob 166.0\n",
"charles NaN\n",
"colin NaN\n",
"darwin 136.0\n",
"dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(s2.keys())\n",
"print(s3.keys())\n",
"\n",
"s2 + s3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Series` kết quả chứa sự kết hợp của các nhãn chỉ mục từ `s2` và `s3`. Vì `\"colin\"` bị thiếu trong `s2` và `\"charles\"` bị thiếu trong `s3`, nên các mục này có giá trị kết quả `NaN`. (tức là. Not-a-Number có nghĩa là *thiếu số*).\n",
"\n",
"Căn chỉnh tự động rất tiện dụng khi làm việc với dữ liệu có thể đến từ nhiều nguồn khác nhau với cấu trúc khác nhau và các mục bị thiếu. Nhưng nếu bạn quên đặt đúng nhãn chỉ mục, bạn có thể nhận được kết quả khá ngạc nhiên:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"s2 = [ 68 83 112 68]\n",
"s5 = [1000 1000 1000 1000]\n"
]
},
{
"data": {
"text/plain": [
"alice NaN\n",
"bob NaN\n",
"charles NaN\n",
"darwin NaN\n",
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
"dtype: float64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s5 = pd.Series([1000,1000,1000,1000])\n",
"print(\"s2 =\", s2.values)\n",
"print(\"s5 =\", s5.values)\n",
"\n",
"s2 + s5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas không thể căn chỉnh `Series`, vì nhãn của chúng hoàn toàn không khớp, do đó kết quả chứa đầy `NaN`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Khởi tạo với số vô hướng\n",
"Bạn cũng có thể khởi tạo đối tượng `Series` bằng cách sử dụng số vô hướng và danh sách các nhãn chỉ mục: tất cả các mục sẽ được đặt thành số vô hướng."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"life 42\n",
"universe 42\n",
"everything 42\n",
"dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
"meaning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tên `Series`\n",
"`Series` có thể có `tên`:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"bob 83\n",
"alice 68\n",
"Name: weights, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
"s6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vẽ đồ thị `Series`\n",
"Pandas giúp dễ dàng vẽ biểu đồ dữ liệu `Series` bằng matplotlib (để biết thêm chi tiết về matplotlib, hãy xem [hướng dẫn matplotlib](tools_matplotlib.ipynb)). Chỉ cần nhập matplotlib và gọi phương thức `plot()`:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
"s7 = pd.Series(temperatures, name=\"Temperature\")\n",
"s7.plot()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Có *nhiều* tùy chọn để vẽ biểu đồ dữ liệu của bạn. Không cần thiết phải liệt kê tất cả chúng ở đây: nếu bạn cần một loại biểu đồ cụ thể (biểu đồ tần số, biểu đồ hình tròn, v.v.), chỉ cần tìm nó trong [Trực quan hóa](http://pandas.pydata.org/pandas-docs/stable/visualization.html) trong tài liệu của pandas và xem mã ví dụ."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Xử lý thời gian\n",
"Nhiều bộ dữ liệu có mốc thời gian và pandas rất giỏi trong thao tác với các dữ liệu đó:\n",
"* nó có thể đại diện cho các khoảng thời gian (chẳng hạn như 2016Q3) và tần suất (chẳng hạn như \"hàng tháng\"),\n",
"* nó có thể chuyển đổi các khoảng thời gian thành mốc thời gian thực và *ngược lại*,\n",
"* nó có thể lấy mẫu lại dữ liệu và tổng hợp các giá trị theo bất kỳ cách nào bạn muốn.\n",
"* nó có thể xử lý các múi giờ.\n",
"\n",
"## Khoảng thời gian\n",
"Hãy bắt đầu bằng cách tạo một chuỗi thời gian bằng cách sử dụng `pd.date_range()`. Thao tác này trả về một `DatetimeIndex` chứa mỗi giờ trong 12 giờ bắt đầu từ ngày 29 tháng 10 năm 2016 lúc 5:30 chiều."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
" '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
" '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
" '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
" '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
" '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
" dtype='datetime64[ns]', freq='H')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')\n",
"dates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`DatetimeIndex` này có thể được sử dụng làm chỉ mục trong `Series`:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00 4.4\n",
"2016-10-29 18:30:00 5.1\n",
"2016-10-29 19:30:00 6.1\n",
"2016-10-29 20:30:00 6.2\n",
"2016-10-29 21:30:00 6.1\n",
"2016-10-29 22:30:00 6.1\n",
"2016-10-29 23:30:00 5.7\n",
"2016-10-30 00:30:00 5.2\n",
"2016-10-30 01:30:00 4.7\n",
"2016-10-30 02:30:00 4.1\n",
"2016-10-30 03:30:00 3.9\n",
"2016-10-30 04:30:00 3.5\n",
"Freq: H, dtype: float64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series = pd.Series(temperatures, dates)\n",
"temp_series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hãy vẽ đồ thị Series này:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"temp_series.plot(kind=\"bar\")\n",
"\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lấy mẫu lại\n",
"Pandas cho phép chúng ta dễ dàng lấy mẫu lại một chuỗi thời gian. Chỉ cần gọi phương thức `resample()` và chỉ định một tần suất mới:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndexResampler [freq=<2 * Hours>, axis=0, closed=left, label=left, convention=start, base=0]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_2H = temp_series.resample(\"2H\")\n",
"temp_series_freq_2H"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hoạt động lấy mẫu lại thực ra là một hoạt động bị hoãn, đó là lý do tại sao chúng ta không nhận được đối tượng `Series` mà thay vào đó là đối tượng `DatetimeIndexResampler`. Để thực sự thực hiện thao tác lấy mẫu lại, chúng ta chỉ cần gọi phương thức `mean()`: Pandas sẽ tính giá trị trung bình của mỗi cặp giờ liên tiếp:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"temp_series_freq_2H = temp_series_freq_2H.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hãy vẽ kết quả:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"temp_series_freq_2H.plot(kind=\"bar\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lưu ý cách các giá trị tự động được tổng hợp thành các khoảng thời gian 2 giờ. Ví dụ: nếu chúng ta xem xét khoảng thời gian 6-8 giờ tối, thì ta có giá trị là `5,1` lúc 6:30 chiều và `6,1` lúc 7:30 tối. Sau khi lấy mẫu lại, chúng ta chỉ có một giá trị là `5.6`, là giá trị trung bình của `5.1` và `6.1`. Thay vì tính toán giá trị trung bình, chúng ta có thể sử dụng bất kỳ hàm tổng hợp nào khác, ví dụ: chúng ta có thể quyết định giữ giá trị nhỏ nhất của mỗi khoảng thời gian:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 16:00:00 4.4\n",
"2016-10-29 18:00:00 5.1\n",
"2016-10-29 20:00:00 6.1\n",
"2016-10-29 22:00:00 5.7\n",
"2016-10-30 00:00:00 4.7\n",
"2016-10-30 02:00:00 3.9\n",
"2016-10-30 04:00:00 3.5\n",
"Freq: 2H, dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_2H = temp_series.resample(\"2H\").min()\n",
"temp_series_freq_2H"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hoặc tương tự, chúng ta có thể sử dụng phương thức `apply()` để thay thế:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 16:00:00 4.4\n",
"2016-10-29 18:00:00 5.1\n",
"2016-10-29 20:00:00 6.1\n",
"2016-10-29 22:00:00 5.7\n",
"2016-10-30 00:00:00 4.7\n",
"2016-10-30 02:00:00 3.9\n",
"2016-10-30 04:00:00 3.5\n",
"Freq: 2H, dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_2H = temp_series.resample(\"2H\").apply(np.min)\n",
"temp_series_freq_2H"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lấy mẫu lên và nội suy\n",
"Ở trên là một ví dụ về lấy mẫu xuống. Chúng ta cũng có thể lấy mẫu lên (tức là tăng tần suất), nhưng điều này tạo ra lỗ hổng trong dữ liệu:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00 4.4\n",
"2016-10-29 17:45:00 NaN\n",
"2016-10-29 18:00:00 NaN\n",
"2016-10-29 18:15:00 NaN\n",
"2016-10-29 18:30:00 5.1\n",
"2016-10-29 18:45:00 NaN\n",
"2016-10-29 19:00:00 NaN\n",
"2016-10-29 19:15:00 NaN\n",
"2016-10-29 19:30:00 6.1\n",
"2016-10-29 19:45:00 NaN\n",
"Freq: 15T, dtype: float64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
"temp_series_freq_15min.head(n=10) # `head` displays the top n values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Một giải pháp là lấp đầy khoảng trống bằng cách nội suy. Chúng ta chỉ cần gọi phương thức `interpolate()`. Mặc định là sử dụng phép nội suy tuyến tính, nhưng chúng ta cũng có thể chọn một phương pháp khác, chẳng hạn như phép nội suy bậc ba:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00 4.400000\n",
"2016-10-29 17:45:00 4.452911\n",
"2016-10-29 18:00:00 4.605113\n",
"2016-10-29 18:15:00 4.829758\n",
"2016-10-29 18:30:00 5.100000\n",
"2016-10-29 18:45:00 5.388992\n",
"2016-10-29 19:00:00 5.669887\n",
"2016-10-29 19:15:00 5.915839\n",
"2016-10-29 19:30:00 6.100000\n",
"2016-10-29 19:45:00 6.203621\n",
"Freq: 15T, dtype: float64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
"temp_series_freq_15min.head(n=10)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"temp_series.plot(label=\"Period: 1 hour\")\n",
"temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Múi giờ\n",
"Theo mặc định, thời gian dữ liệu là *sơ khai*: chúng không biết múi giờ, vì vậy 2016-10-30 02:30 có thể có nghĩa là ngày 30 tháng 10 năm 2016 lúc 2:30 sáng ở Paris hoặc ở New York. Chúng ta có thể chỉ định múi giờ bằng cách gọi phương thức `tz_localize()`:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00-04:00 4.4\n",
"2016-10-29 18:30:00-04:00 5.1\n",
"2016-10-29 19:30:00-04:00 6.1\n",
"2016-10-29 20:30:00-04:00 6.2\n",
"2016-10-29 21:30:00-04:00 6.1\n",
"2016-10-29 22:30:00-04:00 6.1\n",
"2016-10-29 23:30:00-04:00 5.7\n",
"2016-10-30 00:30:00-04:00 5.2\n",
"2016-10-30 01:30:00-04:00 4.7\n",
"2016-10-30 02:30:00-04:00 4.1\n",
"2016-10-30 03:30:00-04:00 3.9\n",
"2016-10-30 04:30:00-04:00 3.5\n",
"Freq: H, dtype: float64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
"temp_series_ny"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lưu ý rằng `-04:00` hiện được thêm vào tất cả các mốc thời gian. Điều này có nghĩa là các mốc thời gian này đề cập đến [UTC](https://en.wikipedia.org/wiki/Coordined_Universal_Time) - 4 giờ.\n",
"\n",
"Chúng ta có thể chuyển đổi các ngày giờ này sang giờ Paris như sau:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 23:30:00+02:00 4.4\n",
"2016-10-30 00:30:00+02:00 5.1\n",
"2016-10-30 01:30:00+02:00 6.1\n",
"2016-10-30 02:30:00+02:00 6.2\n",
"2016-10-30 02:30:00+01:00 6.1\n",
"2016-10-30 03:30:00+01:00 6.1\n",
"2016-10-30 04:30:00+01:00 5.7\n",
"2016-10-30 05:30:00+01:00 5.2\n",
"2016-10-30 06:30:00+01:00 4.7\n",
"2016-10-30 07:30:00+01:00 4.1\n",
"2016-10-30 08:30:00+01:00 3.9\n",
"2016-10-30 09:30:00+01:00 3.5\n",
"Freq: H, dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
"temp_series_paris"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bạn có thể nhận thấy rằng phần bù UTC thay đổi từ `+02:00` thành `+01:00`: điều này là do Pháp chuyển sang giờ mùa đông lúc 3 giờ sáng vào đêm cụ thể đó (thời gian quay trở lại 2 giờ sáng). Lưu ý rằng 2:30 sáng xảy ra hai lần! Hãy quay lại với một biểu diễn sơ khai (nếu bạn ghi nhật ký một số dữ liệu hàng giờ bằng giờ địa phương mà không lưu trữ múi giờ, bạn có thể nhận được kết quả như sau):"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 23:30:00 4.4\n",
"2016-10-30 00:30:00 5.1\n",
"2016-10-30 01:30:00 6.1\n",
"2016-10-30 02:30:00 6.2\n",
"2016-10-30 02:30:00 6.1\n",
"2016-10-30 03:30:00 6.1\n",
"2016-10-30 04:30:00 5.7\n",
"2016-10-30 05:30:00 5.2\n",
"2016-10-30 06:30:00 4.7\n",
"2016-10-30 07:30:00 4.1\n",
"2016-10-30 08:30:00 3.9\n",
"2016-10-30 09:30:00 3.5\n",
"Freq: H, dtype: float64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
"temp_series_paris_naive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bây giờ `02:30` thực sự mơ hồ. Nếu chúng ta cố gắng bản địa hóa những ngày giờ sơ khai này sang múi giờ Paris, ta sẽ gặp lỗi:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Cannot infer dst time from Timestamp('2016-10-30 02:30:00'), try using the 'ambiguous' argument\n"
]
}
],
"source": [
"try:\n",
" temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
"except Exception as e:\n",
" print(type(e))\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"May mắn thay, bằng cách sử dụng đối số `ambiguous`, chúng ta có thể yêu cầu pandas suy ra đúng DST (Daylight Saving Time) dựa trên thứ tự của các dấu thời gian mơ hồ:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 23:30:00+02:00 4.4\n",
"2016-10-30 00:30:00+02:00 5.1\n",
"2016-10-30 01:30:00+02:00 6.1\n",
"2016-10-30 02:30:00+02:00 6.2\n",
"2016-10-30 02:30:00+01:00 6.1\n",
"2016-10-30 03:30:00+01:00 6.1\n",
"2016-10-30 04:30:00+01:00 5.7\n",
"2016-10-30 05:30:00+01:00 5.2\n",
"2016-10-30 06:30:00+01:00 4.7\n",
"2016-10-30 07:30:00+01:00 4.1\n",
"2016-10-30 08:30:00+01:00 3.9\n",
"2016-10-30 09:30:00+01:00 3.5\n",
"Freq: H, dtype: float64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chu kỳ\n",
"Hàm `pd.period_range()` trả về `PeriodIndex` thay vì `DatetimeIndex`. Ví dụ: hãy lấy tất cả các quý trong năm 2016 và 2017:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
" '2017Q3', '2017Q4'],\n",
" dtype='period[Q-DEC]', freq='Q-DEC')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
"quarters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Việc thêm một số `N` vào `PeriodIndex` sẽ dịch chuyển các khoảng thời gian bằng `N` lần tần suất của `PeriodIndex`:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
" '2018Q2', '2018Q3'],\n",
" dtype='period[Q-DEC]', freq='Q-DEC')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters + 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Phương thức `asfreq()` cho phép chúng ta thay đổi tần số của `PeriodIndex`. Tất cả các giai đoạn được kéo dài hoặc rút ngắn cho phù hợp. Ví dụ: hãy chuyển đổi tất cả các khoảng thời gian hàng quý thành khoảng thời gian hàng tháng (phóng đại):"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
" '2017-09', '2017-12'],\n",
" dtype='period[M]', freq='M')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters.asfreq(\"M\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Theo mặc định, `asfreq` phóng đại vào cuối mỗi khoảng thời gian. Thay vào đó, chúng ta có thể yêu cầu nó phóng đại vào đầu mỗi khoảng thời gian:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
" '2017-07', '2017-10'],\n",
" dtype='period[M]', freq='M')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters.asfreq(\"M\", how=\"start\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Và chúng ta có thể thu nhỏ:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters.asfreq(\"A\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tất nhiên chúng ta có thể tạo `Series` với `PeriodIndex`:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016Q1 300\n",
"2016Q2 320\n",
"2016Q3 290\n",
"2016Q4 390\n",
"2017Q1 320\n",
"2017Q2 360\n",
"2017Q3 310\n",
"2017Q4 410\n",
"Freq: Q-DEC, dtype: int64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index = quarters)\n",
"quarterly_revenue"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"quarterly_revenue.plot(kind=\"line\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chúng ta có thể chuyển đổi các khoảng thời gian thành mốc thời gian bằng cách gọi `to_timestamp`. Theo mặc định, điều này sẽ cung cấp cho chúng ta ngày đầu tiên của mỗi khoảng thời gian, nhưng bằng cách đặt `how` và `freq`, chúng ta có thể nhận được giờ cuối cùng của mỗi khoảng thời gian:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-03-31 23:00:00 300\n",
"2016-06-30 23:00:00 320\n",
"2016-09-30 23:00:00 290\n",
"2016-12-31 23:00:00 390\n",
"2017-03-31 23:00:00 320\n",
"2017-06-30 23:00:00 360\n",
"2017-09-30 23:00:00 310\n",
"2017-12-31 23:00:00 410\n",
"Freq: Q-DEC, dtype: int64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"H\")\n",
"last_hours"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Và quay lại thời gian bằng cách gọi `to_period`:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016Q1 300\n",
"2016Q2 320\n",
"2016Q3 290\n",
"2016Q4 390\n",
"2017Q1 320\n",
"2017Q2 360\n",
"2017Q3 310\n",
"2017Q4 410\n",
"Freq: Q-DEC, dtype: int64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"last_hours.to_period()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas cũng cung cấp nhiều chức năng khác liên quan đến thời gian mà chúng tôi khuyên bạn nên xem trong [tài liệu](http://pandas.pydata.org/pandas-docs/stable/timeseries.html). Để kích thích sự tò mò của bạn, đây là một cách để có được ngày làm việc cuối cùng của mỗi tháng trong năm 2016, lúc 9 giờ sáng:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
" '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
" '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
" '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
" dtype='period[H]', freq='H')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
"one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
"last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
"last_bdays.to_period(\"H\") + 9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Đối tượng `DataFrame`\n",
"Một đối tượng DataFrame đại diện cho một bảng tính, với các giá trị ô, tên cột và nhãn chỉ mục hàng. Bạn có thể xác định các biểu thức để tính toán cột dựa trên các cột khác, tạo bảng tổng hợp, nhóm hàng, vẽ biểu đồ, v.v. Bạn có thể xem `DataFrame` là từ điển của `Series`.\n",
"\n",
"## Tạo `DataFrame`\n",
"Bạn có thể tạo DataFrame bằng cách chuyển từ điển các đối tượng `Series`:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
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" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" True | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
" False | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" True | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
" False | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" False | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" False | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" False | \n",
"
\n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
" False | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" body_mass_index | \n",
" height | \n",
" hobby | \n",
" over 30 | \n",
" overweight | \n",
" pets | \n",
" weight | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 33 | \n",
" 22.985398 | \n",
" 172 | \n",
" Biking | \n",
" True | \n",
" False | \n",
" NaN | \n",
" 68 | \n",
"
\n",
" \n",
" | bob | \n",
" 34 | \n",
" 25.335002 | \n",
" 181 | \n",
" Dancing | \n",
" True | \n",
" False | \n",
" 0.0 | \n",
" 83 | \n",
"
\n",
" \n",
" | charles | \n",
" 26 | \n",
" 32.724617 | \n",
" 185 | \n",
" NaN | \n",
" False | \n",
" True | \n",
" 5.0 | \n",
" 112 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" body_mass_index | \n",
" height | \n",
" hobby | \n",
" over 30 | \n",
" overweight | \n",
" pets | \n",
" weight | \n",
"
\n",
" \n",
" \n",
" \n",
" | charles | \n",
" 26 | \n",
" 32.724617 | \n",
" 185 | \n",
" NaN | \n",
" False | \n",
" True | \n",
" 5.0 | \n",
" 112 | \n",
"
\n",
" \n",
" | alice | \n",
" 33 | \n",
" 22.985398 | \n",
" 172 | \n",
" Biking | \n",
" True | \n",
" False | \n",
" NaN | \n",
" 68 | \n",
"
\n",
" \n",
" | bob | \n",
" 34 | \n",
" 25.335002 | \n",
" 181 | \n",
" Dancing | \n",
" True | \n",
" False | \n",
" 0.0 | \n",
" 83 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8 | \n",
" 8 | \n",
" 9 | \n",
"
\n",
" \n",
" | bob | \n",
" 10 | \n",
" 9 | \n",
" 9 | \n",
"
\n",
" \n",
" | charles | \n",
" 4 | \n",
" 8 | \n",
" 2 | \n",
"
\n",
" \n",
" | darwin | \n",
" 9 | \n",
" 10 | \n",
" 10 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 2.828427 | \n",
" 2.828427 | \n",
" 3.000000 | \n",
"
\n",
" \n",
" | bob | \n",
" 3.162278 | \n",
" 3.000000 | \n",
" 3.000000 | \n",
"
\n",
" \n",
" | charles | \n",
" 2.000000 | \n",
" 2.828427 | \n",
" 1.414214 | \n",
"
\n",
" \n",
" | darwin | \n",
" 3.000000 | \n",
" 3.162278 | \n",
" 3.162278 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 9 | \n",
" 9 | \n",
" 10 | \n",
"
\n",
" \n",
" | bob | \n",
" 11 | \n",
" 10 | \n",
" 10 | \n",
"
\n",
" \n",
" | charles | \n",
" 5 | \n",
" 9 | \n",
" 3 | \n",
"
\n",
" \n",
" | darwin | \n",
" 10 | \n",
" 11 | \n",
" 11 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" True | \n",
" True | \n",
" True | \n",
"
\n",
" \n",
" | bob | \n",
" True | \n",
" True | \n",
" True | \n",
"
\n",
" \n",
" | charles | \n",
" False | \n",
" True | \n",
" False | \n",
"
\n",
" \n",
" | darwin | \n",
" True | \n",
" True | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 0.25 | \n",
" -0.75 | \n",
" 1.5 | \n",
"
\n",
" \n",
" | bob | \n",
" 2.25 | \n",
" 0.25 | \n",
" 1.5 | \n",
"
\n",
" \n",
" | charles | \n",
" -3.75 | \n",
" -0.75 | \n",
" -5.5 | \n",
"
\n",
" \n",
" | darwin | \n",
" 1.25 | \n",
" 1.25 | \n",
" 2.5 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
" | bob | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
" | charles | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
" | darwin | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 2.0 | \n",
" 1.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" | charles | \n",
" -4.0 | \n",
" 0.0 | \n",
" -6.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 1.0 | \n",
" 2.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" NaN | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" NaN | \n",
" NaN | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 5.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" NaN | \n",
" 11.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 0.0 | \n",
" 0.0 | \n",
" 9.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 0.0 | \n",
" 5.0 | \n",
" 11.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | colin | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 11.0 | \n",
" 10.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 9.0 | \n",
" 8.0 | \n",
" 8.0 | \n",
"
\n",
" \n",
" | bob | \n",
" NaN | \n",
" 9.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 5.0 | \n",
" 11.0 | \n",
" 4.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" NaN | \n",
" 11.0 | \n",
" 10.0 | \n",
" 9.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" NaN | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" 0.0 | \n",
" 0.5 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 0.0 | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | alice | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 9.0 | \n",
" 8.0 | \n",
" 8.0 | \n",
"
\n",
" \n",
" | bob | \n",
" NaN | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 5.0 | \n",
" 11.0 | \n",
" 4.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" NaN | \n",
" 11.0 | \n",
" 10.0 | \n",
" 9.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
" hobby | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
" NaN | \n",
" Biking | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
" Dancing | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" Dancing | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
" NaN | \n",
" Biking | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" | hobby | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | Biking | \n",
" 8.5 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | Dancing | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" NaN | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" month | \n",
" grade | \n",
" bonus | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" alice | \n",
" sep | \n",
" 8.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 1 | \n",
" alice | \n",
" oct | \n",
" 8.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 2 | \n",
" alice | \n",
" nov | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 3 | \n",
" bob | \n",
" sep | \n",
" 10.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 4 | \n",
" bob | \n",
" oct | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 5 | \n",
" bob | \n",
" nov | \n",
" 10.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | 6 | \n",
" charles | \n",
" sep | \n",
" 4.0 | \n",
" 3.0 | \n",
"
\n",
" \n",
" | 7 | \n",
" charles | \n",
" oct | \n",
" 11.0 | \n",
" 3.0 | \n",
"
\n",
" \n",
" | 8 | \n",
" charles | \n",
" nov | \n",
" 5.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 9 | \n",
" darwin | \n",
" sep | \n",
" 9.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 10 | \n",
" darwin | \n",
" oct | \n",
" 10.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" | 11 | \n",
" darwin | \n",
" nov | \n",
" 11.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" bonus | \n",
" grade | \n",
"
\n",
" \n",
" | name | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 8.333333 | \n",
"
\n",
" \n",
" | bob | \n",
" 1.000000 | \n",
" 9.666667 | \n",
"
\n",
" \n",
" | charles | \n",
" 2.000000 | \n",
" 6.666667 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.333333 | \n",
" 10.000000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" bonus | \n",
" grade | \n",
"
\n",
" \n",
" | name | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 9.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 2.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 1.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | month | \n",
" nov | \n",
" oct | \n",
" sep | \n",
" All | \n",
"
\n",
" \n",
" | name | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 9.00 | \n",
" 8.0 | \n",
" 8.00 | \n",
" 8.333333 | \n",
"
\n",
" \n",
" | bob | \n",
" 10.00 | \n",
" 9.0 | \n",
" 10.00 | \n",
" 9.666667 | \n",
"
\n",
" \n",
" | charles | \n",
" 5.00 | \n",
" 11.0 | \n",
" 4.00 | \n",
" 6.666667 | \n",
"
\n",
" \n",
" | darwin | \n",
" 11.00 | \n",
" 10.0 | \n",
" 9.00 | \n",
" 10.000000 | \n",
"
\n",
" \n",
" | All | \n",
" 8.75 | \n",
" 9.5 | \n",
" 7.75 | \n",
" 8.666667 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" | \n",
" bonus | \n",
" grade | \n",
"
\n",
" \n",
" | name | \n",
" month | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" nov | \n",
" NaN | \n",
" 9.00 | \n",
"
\n",
" \n",
" | oct | \n",
" NaN | \n",
" 8.00 | \n",
"
\n",
" \n",
" | sep | \n",
" NaN | \n",
" 8.00 | \n",
"
\n",
" \n",
" | bob | \n",
" nov | \n",
" 2.000 | \n",
" 10.00 | \n",
"
\n",
" \n",
" | oct | \n",
" NaN | \n",
" 9.00 | \n",
"
\n",
" \n",
" | sep | \n",
" 0.000 | \n",
" 10.00 | \n",
"
\n",
" \n",
" | charles | \n",
" nov | \n",
" 0.000 | \n",
" 5.00 | \n",
"
\n",
" \n",
" | oct | \n",
" 3.000 | \n",
" 11.00 | \n",
"
\n",
" \n",
" | sep | \n",
" 3.000 | \n",
" 4.00 | \n",
"
\n",
" \n",
" | darwin | \n",
" nov | \n",
" 0.000 | \n",
" 11.00 | \n",
"
\n",
" \n",
" | oct | \n",
" 1.000 | \n",
" 10.00 | \n",
"
\n",
" \n",
" | sep | \n",
" 0.000 | \n",
" 9.00 | \n",
"
\n",
" \n",
" | All | \n",
" | \n",
" 1.125 | \n",
" 8.75 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" some_text | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 4 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" Blabla | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
" ... | \n",
" 55.0 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 5 | \n",
" 55.0 | \n",
" 66.0 | \n",
" 99.0 | \n",
" Blabla | \n",
" 154.0 | \n",
" 44.0 | \n",
" 143.0 | \n",
" 77.0 | \n",
" 33.0 | \n",
" 11.0 | \n",
" ... | \n",
" 66.0 | \n",
" 55.0 | \n",
" 66.0 | \n",
" 99.0 | \n",
" 154.0 | \n",
" 44.0 | \n",
" 143.0 | \n",
" 77.0 | \n",
" 33.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
" | 6 | \n",
" 66.0 | \n",
" 77.0 | \n",
" 110.0 | \n",
" Blabla | \n",
" 165.0 | \n",
" 55.0 | \n",
" 154.0 | \n",
" 88.0 | \n",
" 44.0 | \n",
" 22.0 | \n",
" ... | \n",
" 77.0 | \n",
" 66.0 | \n",
" 77.0 | \n",
" 110.0 | \n",
" 165.0 | \n",
" 55.0 | \n",
" 154.0 | \n",
" 88.0 | \n",
" 44.0 | \n",
" 22.0 | \n",
"
\n",
" \n",
" | 7 | \n",
" 77.0 | \n",
" 88.0 | \n",
" 121.0 | \n",
" Blabla | \n",
" NaN | \n",
" 66.0 | \n",
" 165.0 | \n",
" 99.0 | \n",
" 55.0 | \n",
" 33.0 | \n",
" ... | \n",
" 88.0 | \n",
" 77.0 | \n",
" 88.0 | \n",
" 121.0 | \n",
" NaN | \n",
" 66.0 | \n",
" 165.0 | \n",
" 99.0 | \n",
" 55.0 | \n",
" 33.0 | \n",
"
\n",
" \n",
" | 8 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" Blabla | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
" ... | \n",
" 99.0 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
"
\n",
" \n",
" | 9 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" Blabla | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
" ... | \n",
" 110.0 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
"
\n",
" \n",
" | 10 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" Blabla | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
" ... | \n",
" 121.0 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
"
\n",
" \n",
" | 11 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" Blabla | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
" ... | \n",
" 132.0 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
"
\n",
" \n",
" | 12 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" Blabla | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
" ... | \n",
" 143.0 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
"
\n",
" \n",
" | 13 | \n",
" 143.0 | \n",
" 154.0 | \n",
" NaN | \n",
" Blabla | \n",
" 55.0 | \n",
" 132.0 | \n",
" 44.0 | \n",
" 165.0 | \n",
" 121.0 | \n",
" 99.0 | \n",
" ... | \n",
" 154.0 | \n",
" 143.0 | \n",
" 154.0 | \n",
" NaN | \n",
" 55.0 | \n",
" 132.0 | \n",
" 44.0 | \n",
" 165.0 | \n",
" 121.0 | \n",
" 99.0 | \n",
"
\n",
" \n",
" | 14 | \n",
" 154.0 | \n",
" 165.0 | \n",
" 11.0 | \n",
" Blabla | \n",
" 66.0 | \n",
" 143.0 | \n",
" 55.0 | \n",
" NaN | \n",
" 132.0 | \n",
" 110.0 | \n",
" ... | \n",
" 165.0 | \n",
" 154.0 | \n",
" 165.0 | \n",
" 11.0 | \n",
" 66.0 | \n",
" 143.0 | \n",
" 55.0 | \n",
" NaN | \n",
" 132.0 | \n",
" 110.0 | \n",
"
\n",
" \n",
" | 15 | \n",
" 165.0 | \n",
" NaN | \n",
" 22.0 | \n",
" Blabla | \n",
" 77.0 | \n",
" 154.0 | \n",
" 66.0 | \n",
" NaN | \n",
" 143.0 | \n",
" 121.0 | \n",
" ... | \n",
" NaN | \n",
" 165.0 | \n",
" NaN | \n",
" 22.0 | \n",
" 77.0 | \n",
" 154.0 | \n",
" 66.0 | \n",
" NaN | \n",
" 143.0 | \n",
" 121.0 | \n",
"
\n",
" \n",
" | 16 | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" Blabla | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
"
\n",
" \n",
" | 17 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 18 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 19 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 20 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 21 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" Blabla | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
" ... | \n",
" 55.0 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 22 | \n",
" 55.0 | \n",
" 66.0 | \n",
" 99.0 | \n",
" Blabla | \n",
" 154.0 | \n",
" 44.0 | \n",
" 143.0 | \n",
" 77.0 | \n",
" 33.0 | \n",
" 11.0 | \n",
" ... | \n",
" 66.0 | \n",
" 55.0 | \n",
" 66.0 | \n",
" 99.0 | \n",
" 154.0 | \n",
" 44.0 | \n",
" 143.0 | \n",
" 77.0 | \n",
" 33.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
" | 23 | \n",
" 66.0 | \n",
" 77.0 | \n",
" 110.0 | \n",
" Blabla | \n",
" 165.0 | \n",
" 55.0 | \n",
" 154.0 | \n",
" 88.0 | \n",
" 44.0 | \n",
" 22.0 | \n",
" ... | \n",
" 77.0 | \n",
" 66.0 | \n",
" 77.0 | \n",
" 110.0 | \n",
" 165.0 | \n",
" 55.0 | \n",
" 154.0 | \n",
" 88.0 | \n",
" 44.0 | \n",
" 22.0 | \n",
"
\n",
" \n",
" | 24 | \n",
" 77.0 | \n",
" 88.0 | \n",
" 121.0 | \n",
" Blabla | \n",
" NaN | \n",
" 66.0 | \n",
" 165.0 | \n",
" 99.0 | \n",
" 55.0 | \n",
" 33.0 | \n",
" ... | \n",
" 88.0 | \n",
" 77.0 | \n",
" 88.0 | \n",
" 121.0 | \n",
" NaN | \n",
" 66.0 | \n",
" 165.0 | \n",
" 99.0 | \n",
" 55.0 | \n",
" 33.0 | \n",
"
\n",
" \n",
" | 25 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" Blabla | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
" ... | \n",
" 99.0 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
"
\n",
" \n",
" | 26 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" Blabla | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
" ... | \n",
" 110.0 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
"
\n",
" \n",
" | 27 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" Blabla | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
" ... | \n",
" 121.0 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
"
\n",
" \n",
" | 28 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" Blabla | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
" ... | \n",
" 132.0 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
"
\n",
" \n",
" | 29 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" Blabla | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
" ... | \n",
" 143.0 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 9970 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" Blabla | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
" ... | \n",
" 99.0 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
"
\n",
" \n",
" | 9971 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" Blabla | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
" ... | \n",
" 110.0 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
"
\n",
" \n",
" | 9972 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" Blabla | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
" ... | \n",
" 121.0 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
"
\n",
" \n",
" | 9973 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" Blabla | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
" ... | \n",
" 132.0 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
"
\n",
" \n",
" | 9974 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" Blabla | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
" ... | \n",
" 143.0 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
"
\n",
" \n",
" | 9975 | \n",
" 143.0 | \n",
" 154.0 | \n",
" NaN | \n",
" Blabla | \n",
" 55.0 | \n",
" 132.0 | \n",
" 44.0 | \n",
" 165.0 | \n",
" 121.0 | \n",
" 99.0 | \n",
" ... | \n",
" 154.0 | \n",
" 143.0 | \n",
" 154.0 | \n",
" NaN | \n",
" 55.0 | \n",
" 132.0 | \n",
" 44.0 | \n",
" 165.0 | \n",
" 121.0 | \n",
" 99.0 | \n",
"
\n",
" \n",
" | 9976 | \n",
" 154.0 | \n",
" 165.0 | \n",
" 11.0 | \n",
" Blabla | \n",
" 66.0 | \n",
" 143.0 | \n",
" 55.0 | \n",
" NaN | \n",
" 132.0 | \n",
" 110.0 | \n",
" ... | \n",
" 165.0 | \n",
" 154.0 | \n",
" 165.0 | \n",
" 11.0 | \n",
" 66.0 | \n",
" 143.0 | \n",
" 55.0 | \n",
" NaN | \n",
" 132.0 | \n",
" 110.0 | \n",
"
\n",
" \n",
" | 9977 | \n",
" 165.0 | \n",
" NaN | \n",
" 22.0 | \n",
" Blabla | \n",
" 77.0 | \n",
" 154.0 | \n",
" 66.0 | \n",
" NaN | \n",
" 143.0 | \n",
" 121.0 | \n",
" ... | \n",
" NaN | \n",
" 165.0 | \n",
" NaN | \n",
" 22.0 | \n",
" 77.0 | \n",
" 154.0 | \n",
" 66.0 | \n",
" NaN | \n",
" 143.0 | \n",
" 121.0 | \n",
"
\n",
" \n",
" | 9978 | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" Blabla | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
"
\n",
" \n",
" | 9979 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 9980 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 9981 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 9982 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 9983 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" Blabla | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
" ... | \n",
" 55.0 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 9984 | \n",
" 55.0 | \n",
" 66.0 | \n",
" 99.0 | \n",
" Blabla | \n",
" 154.0 | \n",
" 44.0 | \n",
" 143.0 | \n",
" 77.0 | \n",
" 33.0 | \n",
" 11.0 | \n",
" ... | \n",
" 66.0 | \n",
" 55.0 | \n",
" 66.0 | \n",
" 99.0 | \n",
" 154.0 | \n",
" 44.0 | \n",
" 143.0 | \n",
" 77.0 | \n",
" 33.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
" | 9985 | \n",
" 66.0 | \n",
" 77.0 | \n",
" 110.0 | \n",
" Blabla | \n",
" 165.0 | \n",
" 55.0 | \n",
" 154.0 | \n",
" 88.0 | \n",
" 44.0 | \n",
" 22.0 | \n",
" ... | \n",
" 77.0 | \n",
" 66.0 | \n",
" 77.0 | \n",
" 110.0 | \n",
" 165.0 | \n",
" 55.0 | \n",
" 154.0 | \n",
" 88.0 | \n",
" 44.0 | \n",
" 22.0 | \n",
"
\n",
" \n",
" | 9986 | \n",
" 77.0 | \n",
" 88.0 | \n",
" 121.0 | \n",
" Blabla | \n",
" NaN | \n",
" 66.0 | \n",
" 165.0 | \n",
" 99.0 | \n",
" 55.0 | \n",
" 33.0 | \n",
" ... | \n",
" 88.0 | \n",
" 77.0 | \n",
" 88.0 | \n",
" 121.0 | \n",
" NaN | \n",
" 66.0 | \n",
" 165.0 | \n",
" 99.0 | \n",
" 55.0 | \n",
" 33.0 | \n",
"
\n",
" \n",
" | 9987 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" Blabla | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
" ... | \n",
" 99.0 | \n",
" 88.0 | \n",
" 99.0 | \n",
" 132.0 | \n",
" NaN | \n",
" 77.0 | \n",
" NaN | \n",
" 110.0 | \n",
" 66.0 | \n",
" 44.0 | \n",
"
\n",
" \n",
" | 9988 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" Blabla | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
" ... | \n",
" 110.0 | \n",
" 99.0 | \n",
" 110.0 | \n",
" 143.0 | \n",
" 11.0 | \n",
" 88.0 | \n",
" NaN | \n",
" 121.0 | \n",
" 77.0 | \n",
" 55.0 | \n",
"
\n",
" \n",
" | 9989 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" Blabla | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
" ... | \n",
" 121.0 | \n",
" 110.0 | \n",
" 121.0 | \n",
" 154.0 | \n",
" 22.0 | \n",
" 99.0 | \n",
" 11.0 | \n",
" 132.0 | \n",
" 88.0 | \n",
" 66.0 | \n",
"
\n",
" \n",
" | 9990 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" Blabla | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
" ... | \n",
" 132.0 | \n",
" 121.0 | \n",
" 132.0 | \n",
" 165.0 | \n",
" 33.0 | \n",
" 110.0 | \n",
" 22.0 | \n",
" 143.0 | \n",
" 99.0 | \n",
" 77.0 | \n",
"
\n",
" \n",
" | 9991 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" Blabla | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
" ... | \n",
" 143.0 | \n",
" 132.0 | \n",
" 143.0 | \n",
" NaN | \n",
" 44.0 | \n",
" 121.0 | \n",
" 33.0 | \n",
" 154.0 | \n",
" 110.0 | \n",
" 88.0 | \n",
"
\n",
" \n",
" | 9992 | \n",
" 143.0 | \n",
" 154.0 | \n",
" NaN | \n",
" Blabla | \n",
" 55.0 | \n",
" 132.0 | \n",
" 44.0 | \n",
" 165.0 | \n",
" 121.0 | \n",
" 99.0 | \n",
" ... | \n",
" 154.0 | \n",
" 143.0 | \n",
" 154.0 | \n",
" NaN | \n",
" 55.0 | \n",
" 132.0 | \n",
" 44.0 | \n",
" 165.0 | \n",
" 121.0 | \n",
" 99.0 | \n",
"
\n",
" \n",
" | 9993 | \n",
" 154.0 | \n",
" 165.0 | \n",
" 11.0 | \n",
" Blabla | \n",
" 66.0 | \n",
" 143.0 | \n",
" 55.0 | \n",
" NaN | \n",
" 132.0 | \n",
" 110.0 | \n",
" ... | \n",
" 165.0 | \n",
" 154.0 | \n",
" 165.0 | \n",
" 11.0 | \n",
" 66.0 | \n",
" 143.0 | \n",
" 55.0 | \n",
" NaN | \n",
" 132.0 | \n",
" 110.0 | \n",
"
\n",
" \n",
" | 9994 | \n",
" 165.0 | \n",
" NaN | \n",
" 22.0 | \n",
" Blabla | \n",
" 77.0 | \n",
" 154.0 | \n",
" 66.0 | \n",
" NaN | \n",
" 143.0 | \n",
" 121.0 | \n",
" ... | \n",
" NaN | \n",
" 165.0 | \n",
" NaN | \n",
" 22.0 | \n",
" 77.0 | \n",
" 154.0 | \n",
" 66.0 | \n",
" NaN | \n",
" 143.0 | \n",
" 121.0 | \n",
"
\n",
" \n",
" | 9995 | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" Blabla | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
"
\n",
" \n",
" | 9996 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 9997 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 9998 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 9999 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
10000 rows × 27 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" some_text | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 4 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" Blabla | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
" ... | \n",
" 55.0 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 27 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" some_text | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | 9998 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 9999 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
2 rows × 27 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" J | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 8823.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8823.000000 | \n",
" 8823.000000 | \n",
" ... | \n",
" 8824.000000 | \n",
" 8823.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8823.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 87.977559 | \n",
" 87.972575 | \n",
" 87.987534 | \n",
" 88.012466 | \n",
" 87.983791 | \n",
" 88.007480 | \n",
" 87.977561 | \n",
" 88.000000 | \n",
" 88.022441 | \n",
" 88.022441 | \n",
" ... | \n",
" 87.972575 | \n",
" 87.977559 | \n",
" 87.972575 | \n",
" 87.987534 | \n",
" 88.012466 | \n",
" 87.983791 | \n",
" 88.007480 | \n",
" 87.977561 | \n",
" 88.000000 | \n",
" 88.022441 | \n",
"
\n",
" \n",
" | std | \n",
" 47.535911 | \n",
" 47.535523 | \n",
" 47.521679 | \n",
" 47.521679 | \n",
" 47.535001 | \n",
" 47.519371 | \n",
" 47.529755 | \n",
" 47.536879 | \n",
" 47.535911 | \n",
" 47.535911 | \n",
" ... | \n",
" 47.535523 | \n",
" 47.535911 | \n",
" 47.535523 | \n",
" 47.521679 | \n",
" 47.521679 | \n",
" 47.535001 | \n",
" 47.519371 | \n",
" 47.529755 | \n",
" 47.536879 | \n",
" 47.535911 | \n",
"
\n",
" \n",
" | min | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" ... | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" ... | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" ... | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" ... | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
"
\n",
" \n",
" | max | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" ... | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
"
\n",
" \n",
"
\n",
"
8 rows × 26 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" weight | \n",
" birthyear | \n",
" children | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 68.5 | \n",
" 1985 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 83.1 | \n",
" 1984 | \n",
" 3.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" weight | \n",
" birthyear | \n",
" children | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 68.5 | \n",
" 1985 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 83.1 | \n",
" 1984 | \n",
" 3.0 | \n",
"
\n",
" \n",
"
\n",
"\n",
"# my_df.json\n",
"{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
"\n"
]
}
],
"source": [
"for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
" print(\"#\", filename)\n",
" with open(filename, \"rt\") as f:\n",
" print(f.read())\n",
" print()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lưu ý rằng chỉ mục được lưu dưới dạng cột đầu tiên (không có tên) trong tệp CSV, dưới dạng thẻ `` trong HTML và dưới dạng khóa trong JSON.\n",
"\n",
"Lưu vào các định dạng khác hoạt động rất giống nhau, nhưng một số định dạng yêu cầu cài đặt thêm thư viện. Ví dụ: lưu vào Excel yêu cầu thư viện openpyxl:"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No module named 'openpyxl'\n"
]
}
],
"source": [
"try:\n",
" my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
"except ImportError as e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nạp\n",
"Bây giờ, hãy nạp lại tệp CSV của chúng ta vào `DataFrame`:"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" hobby | \n",
" weight | \n",
" birthyear | \n",
" children | \n",
" \n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 68.5 | \n",
" 1985 | \n",
" NaN | \n",
" \n",
" \n",
" | bob | \n",
" Dancing | \n",
" 83.1 | \n",
" 1984 | \n",
" 3.0 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" State | \n",
" Population | \n",
" lat | \n",
" lon | \n",
" \n",
" \n",
" | City | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" \n",
" \n",
" \n",
" \n",
" | Marysville | \n",
" Washington | \n",
" 63269 | \n",
" 48.051764 | \n",
" -122.177082 | \n",
" \n",
" \n",
" | Perris | \n",
" California | \n",
" 72326 | \n",
" 33.782519 | \n",
" -117.228648 | \n",
" \n",
" \n",
" | Cleveland | \n",
" Ohio | \n",
" 390113 | \n",
" 41.499320 | \n",
" -81.694361 | \n",
" \n",
" \n",
" | Worcester | \n",
" Massachusetts | \n",
" 182544 | \n",
" 42.262593 | \n",
" -71.802293 | \n",
" \n",
" \n",
" | Columbia | \n",
" South Carolina | \n",
" 133358 | \n",
" 34.000710 | \n",
" -81.034814 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" city | \n",
" lat | \n",
" lng | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976 | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710 | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201 | \n",
" Florida | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710.0 | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201.0 | \n",
" Florida | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 5 | \n",
" NaN | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976 | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710 | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201 | \n",
" Florida | \n",
" \n",
" \n",
" | 3 | \n",
" NaN | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" name | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" NaN | \n",
" CA | \n",
" \n",
" \n",
" | 1 | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" NaN | \n",
" NY | \n",
" \n",
" \n",
" | 2 | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" NaN | \n",
" FL | \n",
" \n",
" \n",
" | 3 | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" OH | \n",
" \n",
" \n",
" | 4 | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" UT | \n",
" \n",
" \n",
" | 3 | \n",
" San Francisco | \n",
" NaN | \n",
" NaN | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" | 4 | \n",
" New York | \n",
" NaN | \n",
" NaN | \n",
" 8363710.0 | \n",
" New-York | \n",
" \n",
" \n",
" | 5 | \n",
" Miami | \n",
" NaN | \n",
" NaN | \n",
" 413201.0 | \n",
" Florida | \n",
" \n",
" \n",
" | 6 | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" OH | \n",
" \n",
" \n",
" | 3 | \n",
" San Francisco | \n",
" NaN | \n",
" NaN | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" NaN | \n",
" CA | \n",
" \n",
" \n",
" | 1 | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" NaN | \n",
" NY | \n",
" \n",
" \n",
" | 2 | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" NaN | \n",
" FL | \n",
" \n",
" \n",
" | 3 | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" OH | \n",
" \n",
" \n",
" | 4 | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" UT | \n",
" \n",
" \n",
" | 5 | \n",
" San Francisco | \n",
" NaN | \n",
" NaN | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" | 6 | \n",
" New York | \n",
" NaN | \n",
" NaN | \n",
" 8363710.0 | \n",
" New-York | \n",
" \n",
" \n",
" | 7 | \n",
" Miami | \n",
" NaN | \n",
" NaN | \n",
" 413201.0 | \n",
" Florida | \n",
" \n",
" \n",
" | 8 | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" city | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" \n",
" \n",
" | 3 | \n",
" California | \n",
" San Francisco | \n",
" \n",
" \n",
" | 4 | \n",
" New-York | \n",
" New York | \n",
" \n",
" \n",
" | 5 | \n",
" Florida | \n",
" Miami | \n",
" \n",
" \n",
" | 6 | \n",
" Texas | \n",
" Houston | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" city | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" 808976.0 | \n",
" San Francisco | \n",
" California | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" 8363710.0 | \n",
" New York | \n",
" New-York | \n",
" \n",
" \n",
" | 5 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 413201.0 | \n",
" Miami | \n",
" Florida | \n",
" \n",
" \n",
" | 6 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Houston | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | Cleveland | \n",
" OH | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | Houston | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Texas | \n",
" \n",
" \n",
" | Miami | \n",
" FL | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201.0 | \n",
" Florida | \n",
" \n",
" \n",
" | New York | \n",
" NY | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710.0 | \n",
" New-York | \n",
" \n",
" \n",
" | Salt Lake City | \n",
" UT | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | San Francisco | \n",
" CA | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" NaN | \n",
" CA | \n",
" \n",
" \n",
" | 1 | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" NaN | \n",
" NY | \n",
" \n",
" \n",
" | 2 | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" NaN | \n",
" FL | \n",
" \n",
" \n",
" | 3 | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" OH | \n",
" \n",
" \n",
" | 4 | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" UT | \n",
" \n",
" \n",
" | 3 | \n",
" San Francisco | \n",
" NaN | \n",
" NaN | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" | 4 | \n",
" New York | \n",
" NaN | \n",
" NaN | \n",
" 8363710.0 | \n",
" New-York | \n",
" \n",
" \n",
" | 5 | \n",
" Miami | \n",
" NaN | \n",
" NaN | \n",
" 413201.0 | \n",
" Florida | \n",
" \n",
" \n",
" | 6 | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" eco_code | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" 17 | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" 17 | \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" 34 | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" 20 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" eco_code | \n",
" economy | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" 17 | \n",
" Finance | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" 17 | \n",
" Finance | \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" 34 | \n",
" Tourism | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" 20 | \n",
" Energy | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" eco_code | \n",
" economy | \n",
" \n",
" \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" 34 | \n",
" Tourism | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" 20 | \n",
" Energy | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" 17 | \n",
" Finance | \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" 17 | \n",
" Finance | \n",
" \n",
" \n",
" \n",
" |