当我使用带条件的熊猫DataFrame DROP方法时，如何摆脱恼人的警告？

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from datetime import datetime
df_students_ages = df_students.dropna()
df_students_ages.loc[:, ['birth_year']] = df_students_ages.birthday.apply(lambda x : x.split('-')[0])
#conditional drop
df_students_ages.drop(df_students_ages[df_students_ages.birth_year > '2015'].index, inplace=True)
df_students_ages.drop(df_students_ages[df_students_ages.birth_year < '1920'].index, inplace=True)
df_students_ages.drop(columns='birth_year', inplace=True)
df_students_ages.loc[:, ['birthday']] = df_students_ages.birthday.apply(pd.to_datetime)
def from_dob_to_age(born):
    today = pd.to_datetime(datetime.now().date())
    return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
df_students_ages.loc[:, ['age']] = df_students_ages.birthday.apply(lambda x: from_dob_to_age(x))
df_students_ages.sort_values('age')

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:659: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self.obj[k] = np.nan
~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1745: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  isetter(ilocs[0], value)
~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4163: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return super().drop(

#conditional drop
df_students_ages.drop(df_students_ages[df_students_ages.birth_year > '2015'].index, inplace=True)
df_students_ages.drop(df_students_ages[df_students_ages.birth_year < '1920'].index, inplace=True)
df_students_ages.drop(columns='birth_year', inplace=True)
df_students_ages.loc[:, ['birthday']] = df_students_ages.birthday.apply(pd.to_datetime)

#conditional drop
df_students_ages = df_students_ages.drop(df_students_ages[df_students_ages.birth_year > '2015'].index, inplace=True)
df_students_ages = df_students_ages.drop(df_students_ages[df_students_ages.birth_year < '1920'].index, inplace=True)
df_students_ages = df_students_ages.drop(columns='birth_year', inplace=True)
df_students_ages = df_students_ages.loc[:, ['birthday']] = df_students_ages.birthday.apply(pd.to_datetime)

from datetime import datetime
# this is what has actually changed
df_students_ages = df_students
df_students_ages.dropna(inplace=True)
# new column creation without .loc
df_students_ages['birth_year'] = df_students_ages.birthday.apply(lambda x : x.split('-')[0])
# dropping inplace
df_students_ages.drop(df_students_ages[df_students_ages.birth_year > '2015'].index, inplace=True)
df_students_ages.drop(df_students_ages[df_students_ages.birth_year < '1920'].index, inplace=True)
df_students_ages = df_students_ages.drop(columns='birth_year')
df_students_ages.birthday = df_students_ages.birthday.apply(pd.to_datetime)
def from_dob_to_age(born):
    today = pd.to_datetime(datetime.now().date())

当我使用带条件的熊猫DataFrame DROP方法时，如何摆脱恼人的警告？ 内容来源于 Stack Overflow，遵循 CC BY-SA 4.0 许可协议进行翻译与使用。IT领域专用引擎提供翻译支持 腾讯云小微IT领域专用引擎提供翻译支持

2 个回答