在熊猫数据框中迭代行的不同方式
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
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