在Pandas Dataframe中遍歷行的不同方法
阿新 • • 發佈：2020-10-25
Python是進行資料分析的一種出色語言，主要是因為以資料為中心的Python軟體包具有奇妙的生態系統。Pandas是其中的一種，使匯入和分析資料更加容易。
讓我們看看在PandasDataframe中遍歷行的不同方法：
方法＃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', 'PriyankaGiven Dataframe : Name Age Stream Percentage'], '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])
0 Ankit 21 Math 88方法2：使用資料框的loc []函式。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
# 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"])
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 []函式。
# 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
方法4：使用資料框的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"])
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：使用資料框的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"))
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
方法6：使用資料框的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))
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