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Pandas中迭代DataFrame行的方法总结

作者：python收藏家

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,本文主要为大家介绍了如何在Pandas中迭代DataFrame中的行,有需要的可以参考下

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])

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

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"])

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

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])

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

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"])

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

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"))

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

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))

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

以上就是Pandas中迭代DataFrame行的方法总结的详细内容，更多关于Pandas Dataframe迭代行的资料请关注脚本之家其它相关文章！

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