熊猫数据帧的切片、索引、操作和清洗
原文:https://www.geesforgeks.org/slicing-indexing-operating-and-cleaning-pandas-data frame/
在 Pandas 的帮助下，我们可以对数据集执行许多功能，如切片、索引、操作和清理数据框。
案例 1: 使用 数据框对熊猫数据框进行切片
示例 1: 切片行
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# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
['A.B.D Villers', 38, 74, 3428000],
['V.Kholi', 31, 70, 8428000],
['S.Smith', 34, 80, 4428000],
['C.Gayle', 40, 100, 4528000],
['J.Root', 33, 72, 7028000],
['K.Peterson', 42, 85, 2528000]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=['Name', 'Age', 'Weight', 'Salary'])
# data frame before slicing
df
输出:
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# Slicing rows in data frame
df1 = df.iloc[0:4]
# data frame after slicing
df1
输出:
在上面的例子中，我们从数据帧中分割了行。
示例 2 :切片列
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# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
['A.B.D Villers', 38, 74, 3428000],
['V.Kholi', 31, 70, 8428000],
['S.Smith', 34, 80, 4428000],
['C.Gayle', 40, 100, 4528000],
['J.Root', 33, 72, 7028000],
['K.Peterson', 42, 85, 2528000]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=['Name', 'Age', 'Weight', 'Salary'])
# data frame before slicing
df
输出:
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# Slicing columnss in data frame
df1 = df.iloc[:,0:2]
# data frame after slicing
df1
输出:
在上面的例子中，我们从数据框中分割了列。
情况 2: 索引熊猫数据帧
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# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
['A.B.D Villers', 38, 74, 3428000],
['V.Kholi', 31, 70, 8428000],
['S.Smith', 34, 80, 4428000],
['C.Gayle', 40, 100, 4528000],
['J.Root', 33, 72, 7028000],
['K.Peterson', 42, 85, 2528000]]
# creating a pandas dataframe and indexing it using Aplhabets
df = pd.DataFrame(player_list, columns=['Name', 'Age', 'Weight', 'Salary'],
index=['A', 'B', 'C', 'D', 'E', 'F', 'G'])
# Displaying data frame
df
输出:
在上面的例子中，我们对数据帧进行索引。
案例 3: 操纵熊猫数据帧
数据框的操作可以通过多种方式完成，如应用函数、更改列的数据类型、拆分、向数据框添加行和列等。
示例 1: 使用data frame.assign()将λ函数应用于列
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# importing pandas library
import pandas as pd
# creating and initializing a list
values = [['Rohan', 455], ['Elvish', 250], ['Deepak', 495],
['Sai', 400], ['Radha', 350], ['Vansh', 450]]
# creating a pandas dataframe
df = pd.DataFrame(values, columns=['Name', 'Univ_Marks'])
# Applying lambda function to find percentage of
# 'Univ_Marks' column using df.assign()
df = df.assign(Percentage=lambda x: (x['Univ_Marks'] / 500 * 100))
# displaying the data frame
df
输出:
在上面的例子中，lambda 函数被应用于“Univ_Marks”列，并且在它的帮助下形成了一个新的列“Percentage”。
例 2: 按照升序对数据帧进行排序
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# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
['A.B.D Villers', 38, 74, 3428000],
['V.Kholi', 31, 70, 8428000],
['S.Smith', 34, 80, 4428000],
['C.Gayle', 40, 100, 4528000],
['J.Root', 33, 72, 7028000],
['K.Peterson', 42, 85, 2528000]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=['Name', 'Age', 'Weight', 'Salary'])
# Sorting by column 'Weight'
df.sort_values(by=['Weight'])
输出:
在上面的示例中，我们按照“权重”列对数据框进行排序。
案例 4: 清理熊猫数据框
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# importing pandas and Numpy libraries
import pandas as pd
import numpy as np
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
['A.B.D Villers', np.nan, 74, np.nan],
['V.Kholi', 31, 70, 8428000],
['S.Smith', 34, 80, 4428000],
['C.Gayle', np.nan, 100, np.nan],
[np.nan, 33, np.nan, 7028000],
['K.Peterson', 42, 85, 2528000]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=['Name', 'Age', 'Weight', 'Salary'])
df
输出:
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# Checking for missing values
df.isnull().sum()
输出:
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# dropping or cleaning the missing data
df= df.dropna()
df
输出:
在上面的例子中，我们清除了数据集中所有缺失的值。
