极客教程在Pandas Dataframe中迭代行的不同方法
在这篇文章中，我们将介绍如何在Pandas中对DataFrame的行进行迭代。
如何在Pandas中对数据框架中的行进行迭代
Python是一种进行数据分析的伟大语言，主要是因为以数据为中心的Python软件包的奇妙生态系统。Pandas就是这些包中的一个，它使导入和分析数据变得更加容易。
让我们看看在Pandas Dataframe中迭代行的不同方法。
方法1：使用数据框架的索引属性
# 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])
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
方法2：使用Dataframe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"])
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
方法3：使用DataFrame的iloc[]函数
# 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
方法4：使用Dataframe的iterrows()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"])
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
方法5：使用Dataframe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"))
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
方法6：使用Dataframe的apply()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 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