{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "67b5393b",
"metadata": {},
"source": [
"# 一维数据结构Series对象\n",
"\n",
"Pandas模块中有两种主要的数据结构：一维数据结构Series和二维数据结构DataFrame，这两种数据结构能处理各种常见类型的数据。其中，又以二维数据结构DataFrame最为常用。\n",
"\n",
"在Pandas中，一维数据结构Series可以存储任意类型的数据，包括整数、浮点数、字符串、Python 对象等。"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8bca6859",
"metadata": {},
"source": [
"## Series对象的生成\n",
"\n",
"Pandas模块与NumPy模块需要配合使用。导入相关模块："
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "adbc11b1",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8e4305be",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f46cf58d",
"metadata": {},
"source": [
"Series对象的构造方法为：\n",
"\n",
"```python\n",
"pd.Series(data=None, index=None, dtype=None, name=None)\n",
"```\n",
"\n",
"其中，各参数的含义为：\n",
"- data参数可以是列表、元组或者一维数组，也可以是字典，还可以是标量值；\n",
"- index参数是一个与data大小相同的数组或索引，表示Series对象的标记；\n",
"- 与NumPy数组一样，Series对象中的数据必须是同一类型的，不指定dtype参数时，Pandas会根据data中的数据进行推断。"
]
},
{
"cell_type": "markdown",
"id": "7de0edbf",
"metadata": {},
"source": [
"### 使用数组生成"
]
},
{
"cell_type": "markdown",
"id": "379f3148",
"metadata": {},
"source": [
"使用数组生成Series对象："
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "16c2437a",
"metadata": {},
"outputs": [],
"source": [
"a = pd.Series([1, 2, 3, 4])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ea31c105",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "bde1d730",
"metadata": {},
"source": [
"左栏是该Series对象的标记即index参数需要指定的内容，右边是对应的数据。在不指定index参数的情况下，标记默认是RangeIndex(n)，其中n是data的长度。标记可以用`.index`属性查看："
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "08151532",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RangeIndex(start=0, stop=4, step=1)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.index"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "c782c3f9",
"metadata": {},
"source": [
"可以用标记来索引对应位置的值："
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b063318a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d6b177c3",
"metadata": {},
"source": [
"Series对象的标记类似于字典，因此与数组不同的，Series不支持负数索引。\n",
"\n",
"index参数可以不是整数："
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3d1e7912",
"metadata": {},
"outputs": [],
"source": [
"a = pd.Series([1, 2, 3, 4], index=[\"a\", \"b\", \"c\", \"d\"])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "54b15c7d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"d 4\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6140bc65",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a['b']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5fa2ea7d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['a', 'b', 'c', 'd'], dtype='object')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.index"
]
},
{
"cell_type": "markdown",
"id": "766c1e86",
"metadata": {},
"source": [
"不过Series对象也可以用数字索引："
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f03dd793",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0]"
]
},
{
"cell_type": "markdown",
"id": "489fe8ea",
"metadata": {},
"source": [
"### 使用字典生成"
]
},
{
"cell_type": "markdown",
"id": "4d3ccf6f",
"metadata": {},
"source": [
"使用字典生成Series对象："
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4ceb08b6",
"metadata": {},
"outputs": [],
"source": [
"d = {\"c\": 3, \"b\": 2, \"a\": 1}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ced2103a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"c 3\n",
"b 2\n",
"a 1\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(d)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e61ee367",
"metadata": {},
"source": [
"如果指定了index参数，Pandas会按照参数指定的顺序，从字典中依次读取相应的值，并让不存在的键对应`np.nan`："
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "22a8670d",
"metadata": {},
"outputs": [],
"source": [
"a = pd.Series(d, index=['c', 'd', 'b', 'e'])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c8631195",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"c 3.0\n",
"d NaN\n",
"b 2.0\n",
"e NaN\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3b3fde53",
"metadata": {},
"source": [
"### 使用标量生成\n",
"\n",
"Series对象还可以通过标量生成，通过指定index参数，产生一个指定大小且值全为该标量的Series对象："
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d3ac532d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 5\n",
"1 5\n",
"2 5\n",
"dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(5, index=range(3))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "6d629693",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 5\n",
"b 5\n",
"c 5\n",
"d 5\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(5, index=[\"a\", \"b\", \"c\", \"d\"])"
]
},
{
"cell_type": "markdown",
"id": "af3b4401",
"metadata": {},
"source": [
"## Series对象的使用"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a589103a",
"metadata": {},
"source": [
"Series对象可以像数组或字典一样使用。"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d3400ec0",
"metadata": {},
"source": [
"### 像数组一样使用\n",
"\n",
"Series对象可以从数组中生成，也支持一些数组的操作："
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a790798b",
"metadata": {},
"outputs": [],
"source": [
"s = pd.Series(np.random.randn(5),index=['a', 'b', 'c', 'd', 'e'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "bcf80864",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.511909\n",
"b 0.936458\n",
"c 1.273640\n",
"d 0.406360\n",
"e 0.070895\n",
"dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7d43aaef",
"metadata": {},
"source": [
"虽然标记不是数字，仍然可以像数组一样按照位置顺序对它进行索引："
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ac4a2754",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5119086238174924"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[0]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "f537d078",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.511909\n",
"b 0.936458\n",
"c 1.273640\n",
"dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[:3]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "c8ac0b58",
"metadata": {},
"source": [
"也可以使用布尔值进行索引："
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "6645b00b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b 0.936458\n",
"c 1.273640\n",
"dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s > s.median()]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "37e8a602",
"metadata": {},
"source": [
"Series对象还支持与NumPy数组类似的高级索引，同时索引多个元素："
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "9f809d87",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"e 0.070895\n",
"d 0.406360\n",
"b 0.936458\n",
"dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[[4, 3, 1]]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "759e5aec",
"metadata": {},
"source": [
"一些NumPy函数可以直接作用在Series对象上，返回的结果还是Series对象："
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b08e3728",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.668473\n",
"b 2.550930\n",
"c 3.573839\n",
"d 1.501342\n",
"e 1.073468\n",
"dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(s)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "96e4b631",
"metadata": {},
"source": [
"### 像字典一样使用"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2ae10147",
"metadata": {},
"source": [
"Series对象也可以像字典一样的使用，标记就相当于字典的键，可以进行值的查询："
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "984ede55",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5119086238174924"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s['a']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "0f14b4bd",
"metadata": {},
"outputs": [],
"source": [
"s['e'] = 12"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "e9a26f8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.511909\n",
"b 0.936458\n",
"c 1.273640\n",
"d 0.406360\n",
"e 12.000000\n",
"dtype: float64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6ddcb8db",
"metadata": {},
"source": [
"可以用关键字in查看Series中是否存在某个标记："
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "53181c6e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'e' in s"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "a59d7ef4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0 in s"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6a49b429",
"metadata": {},
"source": [
"Series对象也支持用.get()方法索引不存在的标记："
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "5682c5ac",
"metadata": {},
"outputs": [],
"source": [
"s.get('f')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "769f1cae",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.get('f', np.nan)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f64e019b",
"metadata": {},
"source": [
"### 数学运算和标记对齐"
]
},
{
"cell_type": "markdown",
"id": "87842941",
"metadata": {},
"source": [
"基础的数学运算："
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "cfe3365b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.023817\n",
"b 1.872916\n",
"c 2.547281\n",
"d 0.812719\n",
"e 24.000000\n",
"dtype: float64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s + s"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "f7da1e41",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.023817\n",
"b 1.872916\n",
"c 2.547281\n",
"d 0.812719\n",
"e 24.000000\n",
"dtype: float64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s * 2"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "d4fc5d9d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.668473\n",
"b 2.550930\n",
"c 3.573839\n",
"d 1.501342\n",
"e 162754.791419\n",
"dtype: float64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(s)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4fed949d",
"metadata": {},
"source": [
"不过数组与Series对象有一个本质上的区别。数组只有顺序没有标记，而Series对象是有标记的，两个Series对象相加时，会根据标记的值进行对齐操作。\n",
"\n",
"例如，`s[1:]`的标记为b到e，而`s[:-1]`的标记为a到d，它们相加时，会首先对两个Series中各自独有的部分补上np.nan，然后再相加，从而得到："
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "21fdc61e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b 0.936458\n",
"c 1.273640\n",
"d 0.406360\n",
"e 12.000000\n",
"dtype: float64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[1:]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "48379986",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.511909\n",
"b 0.936458\n",
"c 1.273640\n",
"d 0.406360\n",
"dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[:-1]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "4ab92ce1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a NaN\n",
"b 1.872916\n",
"c 2.547281\n",
"d 0.812719\n",
"e NaN\n",
"dtype: float64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[1:] + s[:-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85a3b0c8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}