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在熊猫数据框中迭代行的不同方式

原文:https://www.geeksforgeeks.org/不同的迭代方式-熊猫中的行-数据框/

Python 是进行数据分析的优秀语言,主要是因为以数据为中心的 Python 包的奇妙生态系统。熊猫就是其中的一个包,让导入和分析数据变得更加容易。

让我们看看熊猫数据框中迭代行的不同方法:

方法#1 : 使用数据框的索引属性。

# import pandas package as pd
import pandas as pd

# Define a dictionary containing students data
data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'],
                'Age': [21, 19, 20, 18],
                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'],
                'Percentage': [88, 92, 95, 70]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", df)

print("\nIterating over rows using index attribute :\n")

# iterate through each row and select 
# 'Name' and 'Stream' column respectively.
for ind in df.index:
     print(df['Name'][ind], df['Stream'][ind])

Output:

Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using index attribute :

Ankit Math
Amit Commerce
Aishwarya Arts
Priyanka Biology

方法 2 : 使用数据框的loc【】功能。

# import pandas package as pd
import pandas as pd

# Define a dictionary containing students data
data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'],
                'Age': [21, 19, 20, 18],
                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'],
                'Percentage': [88, 92, 95, 70]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", df)

print("\nIterating over rows using loc function :\n")

# iterate through each row and select 
# 'Name' and 'Age' column respectively.
for i in range(len(df)) :
  print(df.loc[i, "Name"], df.loc[i, "Age"])

Output:

Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using loc function :

Ankit 21
Amit 19
Aishwarya 20
Priyanka 18

方法#3 : 使用数据框的iloc【】功能。

# import pandas package as pd
import pandas as pd

# Define a dictionary containing students data
data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'],
                'Age': [21, 19, 20, 18],
                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'],
                'Percentage': [88, 92, 95, 70]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", df)

print("\nIterating over rows using iloc function :\n")

# iterate through each row and select 
# 0th and 2nd index column respectively.
for i in range(len(df)) :
  print(df.iloc[i, 0], df.iloc[i, 2])

Output:

Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using iloc function :

Ankit Math
Amit Commerce
Aishwarya Arts
Priyanka Biology

方法#4 : 使用数据框的ITER row()方法。

# import pandas package as pd
import pandas as pd

# Define a dictionary containing students data
data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'],
                'Age': [21, 19, 20, 18],
                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'],
                'Percentage': [88, 92, 95, 70]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", df)

print("\nIterating over rows using iterrows() method :\n")

# iterate through each row and select 
# 'Name' and 'Age' column respectively.
for index, row in df.iterrows():
    print (row["Name"], row["Age"])

Output:

Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using iterrows() method :

Ankit 21
Amit 19
Aishwarya 20
Priyanka 18

方法#5 : 使用数据框的 itertuples() 方法。

# import pandas package as pd
import pandas as pd

# Define a dictionary containing students data
data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'],
                'Age': [21, 19, 20, 18],
                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'],
                'Percentage': [88, 92, 95, 70]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", df)

print("\nIterating over rows using itertuples() method :\n")

# iterate through each row and select 
# 'Name' and 'Percentage' column respectively.
for row in df.itertuples(index = True, name ='Pandas'):
    print (getattr(row, "Name"), getattr(row, "Percentage"))

Output:

Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using itertuples() method :

Ankit 88
Amit 92
Aishwarya 95
Priyanka 70

方法#6 : 使用应用()方法的数据框。

# import pandas package as pd
import pandas as pd

# Define a dictionary containing students data
data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'],
                'Age': [21, 19, 20, 18],
                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'],
                'Percentage': [88, 92, 95, 70]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage'])

print("Given Dataframe :\n", df)

print("\nIterating over rows using apply function :\n")

# iterate through each row and concatenate
# 'Name' and 'Percentage' column respectively.
print(df.apply(lambda row: row["Name"] + " " + str(row["Percentage"]), axis = 1))

Output:

Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using apply function :

0        Ankit 88
1         Amit 92
2    Aishwarya 95
3     Priyanka 70
dtype: object



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