如何在 python 中多次将自定义函数应用于同一数据框？

Question

我正在尝试分解 pandas dataframe 的列以创建新的 columns.

def explode(child_df, column_value):
    child_df = child_df.dropna(subset=[column_value])

    if isinstance(child_df[column_value].iloc[0], str):
        print('tried')
        child_df[column_value] = child_df[column_value].apply(ast.literal_eval)

    expanded_child_df = (pd.concat({i: json_normalize(x) for i, x in child_df.pop(column_value).items()}).reset_index(level=1, drop=True).join(child_df,how='right',lsuffix='_left',rsuffix='_right').reset_index(drop=True))
    expanded_child_df.columns = map(str.lower, expanded_child_df.columns)

    return expanded_child_df

有没有办法多次将 explode 函数应用于数据帧，

这是我尝试将 explode 函数应用于数据框的地方 consolidated_df:

def clean():
    column_value = ['tracking_results','trackable_items','events']
    consolidated_df_cleaner = explode(consolidated_df,column_value.value)
    # Need to iterate over column_value and pass the value as the second argument into `explode` function on the same dataframe
    consolidated_df_cleaner.to_csv('/home/response4.csv',index=False)

试过这个但不行:

pd_list = []
    for param in column_value:
        pd_list.append(apply(explode(consolidated_df),param))

这就是我现在正在做的事情，我需要避免这种情况:

consolidated_df_cleaner=explode(consolidated_df,'tracking_results')
consolidated_df_cleaner2=explode(consolidated_df_cleaner,'trackable_items')
consolidated_df_cleaner3= explode(consolidated_df_cleaner2,'events')
consolidated_df_cleaner3.to_csv('/home/response4.csv',index=False)

预期输出：

tracking_results   trackable_items   events
intransit           abc              22
intransit           xqy              23

Answer 1

尝试


(consolidated_df
    .pipe(explode,'tracking_results')
    .pipe(explode,'trackable_items')
    .pipe(explode,'events')
    .to_csv('/home/response4.csv',index=False)
)

如何在 python 中多次将自定义函数应用于同一数据框？

How to apply a custom function to the same dataframe multiple times in python?

python

python-3.x

pandas

pandas-apply