Question

我们说我有以下pandas DataFrame：

df = pd.DataFrame({'one': ['Baseline', 5, 6], 'two': [10, 10, 10]})
print(df)
print(df.dtypes)
#  one    object
#  two     int64

我想收集df.one != 'Baseline'中的所有行，然后将此新数据框中的one列转换为int数据类型。我认为以下情况可以正常工作，但当我尝试将SettingWithCopyWarning投放到int时，我收到one投诉：

df_sub = df[df['one'] != 'Baseline']
df_sub['one'] = df_sub['one'].astype(int)

script.py:15. 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
   df_sub['one'] = df_sub['one'].astype(int)

代码似乎工作正常（见下文），但我想知道如何避免这种警告（我应该使用不同的方法等）。我关注this question以更改特定列的数据类型。我还尝试了df_sub.loc[:, 'one'] = df_sub['one'].astype(int)和df_sub.loc[:, 'one'] = df_sub.loc[:, 'one'].astype(int)，我也遇到了同样的错误。

print(df_sub.dtypes)
#  one     int64
#  two     int64

Answer 1

为了避免该警告，请复制您的数据框

df_sub = df[df['one'] != 'Baseline'].copy() # A deep copy of your dataframe otherwise it'll point to an existing one in memory.

Pandas：更改列数据类型会产生警告

1 个答案: