22  drop

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'London', 'Paris', 'Berlin']}
df = pd.DataFrame(data)

df

	Name	Age	City
0	Alice	25	New York
1	Bob	30	London
2	Charlie	35	Paris
3	David	40	Berlin

# Drop rows based on a condition
df_dropped = df.drop(df[df['Age'] > 30].index)
df_dropped

	Name	Age	City
0	Alice	25	New York
1	Bob	30	London

# Drop rows based on a boolean mask:
mask = df['Age'] > 30

df_dropped_rows = df.drop(df.index[mask], axis=0)

df_dropped_rows

	Name	Age	City
0	Alice	25	New York
1	Bob	30	London

#Drop rows based on multiple conditions，用逻辑否

df_dropped = df[~((df['Age'] >= 30) & (df['City'] == 'London'))]

df_dropped

	Name	Age	City
0	Alice	25	New York
2	Charlie	35	Paris
3	David	40	Berlin

# Drop rows based on a list of indices
indices_to_drop = [0, 2]
df_dropped = df.drop(indices_to_drop)

df_dropped

	Name	Age	City
1	Bob	30	London
3	David	40	Berlin

# Drop columns based on column names
df_dropped_cols = df.drop(['City'], axis=1)
df_dropped_cols

	Name	Age
0	Alice	25
1	Bob	30
2	Charlie	35
3	David	40

# Drop rows with missing values:
df_dropped = df.dropna()
df_dropped

	Name	Age	City
0	Alice	25	New York
1	Bob	30	London
2	Charlie	35	Paris
3	David	40	Berlin

# Drop rows with duplicate values in a specific column:

df_dropped = df.drop_duplicates(subset='Name')

df_dropped

	Name	Age	City
0	Alice	25	New York
1	Bob	30	London
2	Charlie	35	Paris
3	David	40	Berlin