Pandas Dataframe中迭代行的几种方法

在本文中，我们将介绍如何在Pandas中迭代DataFrame中的行。

Python是进行数据分析的一种很好的语言，主要是因为以数据为中心的Python包的奇妙生态系统。Pandas就是其中之一，它使导入和分析数据变得更加容易。

1. 使用Dataframe的index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])
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输出

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
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2. 使用DataFrame的loc

# 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"])
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输出

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
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3. 使用DataFrame的iloc

# 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])
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输出

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
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4. 使用Dataframe的iterrows()

# 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"])
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输出

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
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5. 使用Dataframe的itertuples()

# 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"))
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输出

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
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6. 使用DataFrame的apply()

# 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))

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输出

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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