拆分 Pandas DataFrame |
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使用行索引分割 DataFrame
使用 groupby() 方法拆分 DataFrame
使用 sample() 方法拆分 DataFrame
本教程解釋瞭如何使用行索引、DataFrame.groupby() 方法和 DataFrame.sample() 方法將一個 DataFrame 分割成多個較小的 DataFrame。 我們將使用下面的 apprix_df DataFrame 來解釋如何將一個 DataFrame 分割成多個更小的 DataFrame。 import pandas as pd apprix_df = pd.DataFrame( { "Name": ["Anish", "Rabindra", "Manish", "Samir", "Binam"], "Post": ["CEO", "CTO", "System Admin", "Consultant", "Engineer"], "Qualification": ["MBA", "MS", "MCA", "PhD", "BE"], } ) print("Apprix Team DataFrame:") print(apprix_df, "\n")輸出： Apprix Team DataFrame: Name Post Qualification 0 Anish CEO MBA 1 Rabindra CTO MS 2 Manish System Admin MCA 3 Samir Consultant PhD 4 Binam Engineer BE 使用行索引分割 DataFrame import pandas as pd apprix_df = pd.DataFrame( { "Name": ["Anish", "Rabindra", "Manish", "Samir", "Binam"], "Post": ["CEO", "CTO", "System Admin", "Consultant", "Engineer"], "Qualification": ["MBA", "MS", "MCA", "PhD", "BE"], } ) print("Apprix Team DataFrame:") print(apprix_df, "\n") apprix_1 = apprix_df.iloc[:2, :] apprix_2 = apprix_df.iloc[2:, :] print("The DataFrames formed by splitting of Apprix Team DataFrame are: ", "\n") print(apprix_1, "\n") print(apprix_2, "\n")輸出： Apprix Team DataFrame: Name Post Qualification 0 Anish CEO MBA 1 Rabindra CTO MS 2 Manish System Admin MCA 3 Samir Consultant PhD 4 Binam Engineer BE The DataFrames formed by splitting the Apprix Team DataFrame are: Name Post Qualification 0 Anish CEO MBA 1 Rabindra CTO MS Name Post Qualification 2 Manish System Admin MCA 3 Samir Consultant PhD 4 Binam Engineer BE它使用行索引將 DataFrame apprix_df 分成兩部分。第一部分包含 apprix_df DataFrame 的前兩行，而第二部分包含最後三行。 我們可以在 iloc 屬性中指定每次分割的行。[:2,:] 表示選擇索引 2 之前的行（索引 2 的行不包括在內）和 DataFrame 中的所有列。因此，apprix_df.iloc[:2,:] 選擇 DataFrame apprix_df 中索引 0 和 1 的前兩行。 使用 groupby() 方法拆分 DataFrame import pandas as pd apprix_df = pd.DataFrame( { "Name": ["Anish", "Rabindra", "Manish", "Samir", "Binam"], "Post": ["CEO", "CTO", "System Admin", "Consultant", "Engineer"], "Qualification": ["MBA", "MS", "MS", "PhD", "MS"], } ) print("Apprix Team DataFrame:") print(apprix_df, "\n") groups = apprix_df.groupby(apprix_df.Qualification) ms_df = groups.get_group("MS") mba_df = groups.get_group("MBA") phd_df = groups.get_group("PhD") print("Group with Qualification MS:") print(ms_df, "\n") print("Group with Qualification MBA:") print(mba_df, "\n") print("Group with Qualification PhD:") print(phd_df, "\n")輸出： Apprix Team DataFrame: Name Post Qualification 0 Anish CEO MBA 1 Rabindra CTO MS 2 Manish System Admin MS 3 Samir Consultant PhD 4 Binam Engineer MS Group with Qualification MS: Name Post Qualification 1 Rabindra CTO MS 2 Manish System Admin MS 4 Binam Engineer MS Group with Qualification MBA: Name Post Qualification 0 Anish CEO MBA Group with Qualification PhD: Name Post Qualification 3 Samir Consultant PhD它根據 Qualification 列的值將 DataFrame apprix_df 分成三部分。Qualification 列值相同的行將被放在同一個組中。 groupby() 函式將根據 Qualification 列的值形成分組。然後我們使用 get_group() 方法提取被 groupby() 方法分組的行。 使用 sample() 方法拆分 DataFrame我們可以通過使用 sample() 方法從 DataFrame 中隨機抽取行來形成一個 DataFrame。我們可以設定從父 DataFrame 中抽取行的比例。 import pandas as pd apprix_df = pd.DataFrame( { "Name": ["Anish", "Rabindra", "Manish", "Samir", "Binam"], "Post": ["CEO", "CTO", "System Admin", "Consultant", "Engineer"], "Qualification": ["MBA", "MS", "MS", "PhD", "MS"], } ) print("Apprix Team DataFrame:") print(apprix_df, "\n") random_df = apprix_df.sample(frac=0.4, random_state=60) print("Random split from the Apprix Team DataFrame:") print(random_df)輸出： Apprix Team DataFrame: Name Post Qualification 0 Anish CEO MBA 1 Rabindra CTO MS 2 Manish System Admin MS 3 Samir Consultant PhD 4 Binam Engineer MS Random split from the Apprix Team DataFrame: Name Post Qualification 0 Anish CEO MBA 4 Binam Engineer MS它從 apprix_df DataFrame 中隨機抽取 40% 的行，然後顯示由抽取的行形成的 DataFrame。設定 random_state 是為了確保每次抽樣都能得到相同的隨機樣本。 |
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