拆分 Pandas DataFrame
Suraj Joshi
2023年1月30日
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。
作者： Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
LinkedIn