{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" # Pandas DataSeries (15 exercises with solution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Write a Python program to create and display a one-dimensional array-like object containing an array of data using Pandas module."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
"dtype: int64\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"ds = pd.Series([1, 2, 3, 4, 5])\n",
"print(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Write a Python program to convert a Panda module Series to Python list and it's type."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"int64\n",
"\n",
"[1, 2, 3, 4, 5]\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"ds = pd.Series([1, 2, 3, 4, 5])\n",
"print(ds.dtype)\n",
"print(type(ds))\n",
"\n",
"series_to_list = list(ds) # We can use ds.tolist() function instead.\n",
"print(series_to_list)\n",
"print(type(series_to_list))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Write a Python program to add, subtract, multiple and divide two Pandas Series. Go to the editorSample Series: [2, 4, 6, 8, 10], [1, 3, 5, 7, 9]."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Summation of two Series:\n",
"0 3\n",
"1 7\n",
"2 11\n",
"3 15\n",
"4 19\n",
"dtype: int64\n",
"\n",
"Difference of two Series:\n",
"0 1\n",
"1 1\n",
"2 1\n",
"3 1\n",
"4 1\n",
"dtype: int64\n",
"\n",
"Multiple of two Series:\n",
"0 2\n",
"1 12\n",
"2 30\n",
"3 56\n",
"4 90\n",
"dtype: int64\n",
"\n",
"Divide of two Series:\n",
"0 2.000000\n",
"1 1.333333\n",
"2 1.200000\n",
"3 1.142857\n",
"4 1.111111\n",
"dtype: float64\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"ds1 = pd.Series([2, 4, 6, 8, 10])\n",
"ds2 = pd.Series([1, 3, 5, 7, 9])\n",
"\n",
"sum = ds1 + ds2\n",
"print(\"Summation of two Series:\")\n",
"print(sum)\n",
"print()\n",
"\n",
"\n",
"print(\"Difference of two Series:\")\n",
"print(ds1 - ds2)\n",
"print()\n",
"\n",
"print(\"Multiple of two Series:\")\n",
"print(ds1 * ds2)\n",
"print()\n",
"\n",
"print(\"Divide of two Series:\")\n",
"print(ds1 / ds2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Write a Pandas program to compare the elements of the two Pandas Series. Sample Series: [2, 4, 6, 8, 10], [1, 3, 5, 7, 9]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Equal Check:\n",
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"dtype: bool\n",
"\n",
"Greater Check:\n",
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"4 True\n",
"dtype: bool\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ds1 = pd.Series([2, 4, 6, 8, 10])\n",
"ds2 = pd.Series([1, 3, 5, 7, 9])\n",
"\n",
"print(\"Equal Check:\")\n",
"print(ds1 == ds2)\n",
"print()\n",
"\n",
"print(\"Greater Check:\")\n",
"print(ds1 > ds2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Write a Python program to convert a dictionary to a Pandas series.\n",
"Original dictionary: {'a': 100, 'b': 200, 'c': 300, 'd': 400, 'e': 800}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 100\n",
"b 200\n",
"c 300\n",
"d 400\n",
"e 800\n",
"dtype: int64\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"sample_dict = {'a': 100, 'b': 200, 'c': 300, 'd': 400, 'e': 800}\n",
"\n",
"dict_to_series = pd.Series(sample_dict)\n",
"print(dict_to_series)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6. Write a Python program to convert a NumPy array to a Pandas series."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10 20 30 40 50]\n",
"\n",
"\n",
"From Numpy array to Pandas Series Conversion:\n",
"0 10\n",
"1 20\n",
"2 30\n",
"3 40\n",
"4 50\n",
"dtype: int32\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"l = [10, 20, 30, 40, 50]\n",
"arr = np.array(l)\n",
"print(arr)\n",
"print(type(arr))\n",
"\n",
"print('\\nFrom Numpy array to Pandas Series Conversion:')\n",
"print(pd.Series(arr))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7. Write a Python program to change the data type of given a column or a Series."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 100.00\n",
"1 200.00\n",
"2 NaN\n",
"3 300.12\n",
"4 400.00\n",
"dtype: float64\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"origina_ser = pd.Series([100, 200, 'python', 300.12, 400])\n",
"\n",
"numeric_ser = pd.to_numeric(origina_ser, errors = 'coerce')\n",
"print(numeric_ser)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 8. Write a Python Pandas program to convert the first column of a DataFrame as a Series."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original Dataframe:\n",
" col1 col2 col3\n",
"0 1 4 7\n",
"1 2 5 5\n",
"2 3 6 8\n",
"3 4 9 12\n",
"4 7 5 1\n",
"5 11 0 11\n",
"\n",
"\n",
"First Column as Series:\n",
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 7\n",
"5 11\n",
"Name: col1, dtype: int64\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"dataframe = pd.DataFrame({'col1': [1, 2, 3, 4, 7, 11], 'col2': [4, 5, 6, 9, 5, 0], 'col3': [7, 5, 8, 12, 1, 11]})\n",
"print('Original Dataframe:')\n",
"print(dataframe)\n",
"print('\\n')\n",
"\n",
"first_column = dataframe['col1']\n",
"print('First Column as Series:')\n",
"print(first_column)\n",
"print(type(first_column))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 9. Write a Pandas program to convert a given Series to an array."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pandas Series to Array: [100, 200, 'python', 300.12, 400]\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ser = pd.Series([100, 200, 'python', 300.12, 400])\n",
"ser_to_array = ser.to_list()\n",
"\n",
"print('Pandas Series to Array:', ser_to_array)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 10. Write a Pandas program to convert Series of lists to one Series."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Converted series of lists into one series:\n",
"\n",
"0 Red\n",
"1 Green\n",
"2 White\n",
"3 Red\n",
"4 Black\n",
"5 Yellow\n",
"dtype: object\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ser = pd.Series([['Red', 'Green', 'White'], ['Red', 'Black'], ['Yellow']])\n",
"\n",
"single_series = ser.apply(pd.Series).stack().reset_index(drop = True) #Learned New Technique\n",
"print('Converted series of lists into one series:\\n')\n",
"print(single_series)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 11. Write a Pandas program to sort a given Series."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sorted Series by values:\n"
]
},
{
"data": {
"text/plain": [
"0 100\n",
"1 200\n",
"3 300.12\n",
"4 400\n",
"2 python\n",
"dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ser = pd.Series(['100', '200', 'python', '300.12', '400'])\n",
"\n",
"print('Sorted Series by values:')\n",
"ser.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 12. Write a Pandas program to add some data to an existing Series."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New Series after adding:\n",
"\n",
"0 100\n",
"1 200\n",
"2 python\n",
"3 300.12\n",
"4 400\n",
"0 500\n",
"1 php\n",
"dtype: object\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ser = pd.Series(['100', '200', 'python', '300.12', '400'])\n",
"\n",
"series_after_adding = ser.append(pd.Series(['500', 'php']))\n",
"print('New Series after adding:\\n')\n",
"print(series_after_adding)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 13. Write a Pandas program to create a subset of a given series based on value and condition."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Subset of series based on condition:\n"
]
},
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ser = pd.Series([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
"\n",
"condition = ser.apply(lambda x: x <= 5)\n",
"\n",
"print('Subset of series based on condition:')\n",
"ser[condition]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 14. Write a Pandas program to change the order of index of a given series."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"B 2\n",
"A 1\n",
"C 3\n",
"D 4\n",
"E 5\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ser = pd.Series([1, 2, 3, 4, 5], index = ['A', 'B', 'C', 'D', 'E'])\n",
"\n",
"new_index = ['B', 'A', 'C', 'D', 'E']\n",
"ser.reindex(new_index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 15. Write a Pandas program to create the mean and standard deviation of the data of a given Series."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of the series\t\t: 4.818181818181818\n",
"Standard deviation of the series: 2.522624895547565\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"ser = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 5, 3])\n",
"print('Mean of the series\\t\\t:', ser.mean())\n",
"print('Standard deviation of the series:', ser.std())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Origin: [https://www.w3resource.com/python-exercises/pandas/index-data-series.php](https://www.w3resource.com/python-exercises/pandas/index-data-series.php)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# THE END "
]
}
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