切片、索引、操作和清理Pandas数据框架

在Pandas的帮助下，我们可以在数据集上执行许多功能，如切片、索引、操作和清理数据帧。

案例1：使用DataFrame.iloc[]对Pandas数据框架进行切片。

例子1：切分行数

# 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:

# Slicing rows in data frame
df1 = df.iloc[0:4]
  
# data frame after slicing
df1

输出:

在上面的例子中，我们将数据框中的行切成片状。

例子2 :分割列

# 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:

# Slicing columnss in data frame
df1 = df.iloc[:,0:2]
  
# data frame after slicing
df1

输出:

在上面的例子中，我们把数据框架中的列切开。

案例2：对Pandas数据框架进行索引

# 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，我们对数据框架进行了索引。

案例3：操纵Pandas数据框架

对数据框架的操作可以通过多种方式进行，如应用函数、改变列的数据类型、拆分、向数据框架添加行和列等。

示例1：使用Dataframe.assign()将lambda函数应用于一个列。

# 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，并在它的帮助下形成一个新的列’Percentity’。

示例2：以升序对数据框进行排序。

# 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:

在上面的例子中，我们按列 “重量 “对数据框进行排序。

案例4：清理Pandas数据框架

# 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:

# Checking for missing values
df.isnull().sum()

输出:

# dropping or cleaning the missing data 
df= df.dropna() 
df

输出:

在上面的例子中，我们清理了数据集中所有的缺失值。