Pandas中迭代DataFrame行的方法總結(jié)
Python是進行數(shù)據(jù)分析的一種很好的語言，主要是因為以數(shù)據(jù)為中心的Python包的奇妙生態(tài)系統(tǒng)。Pandas就是其中之一，它使導(dǎo)入和分析數(shù)據(jù)變得更加容易。
1. 使用Dataframe的index屬性
# 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])輸出
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"])輸出
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])輸出
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"])輸出
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"))輸出
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))輸出
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行的方法總結(jié)的詳細(xì)內(nèi)容，更多關(guān)于Pandas Dataframe迭代行的資料請關(guān)注腳本之家其它相關(guān)文章！
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