我有一个pandas数据框,其日期和字符串与此类似:
Start End Note Item
2016-10-22 2016-11-05 Z A
2017-02-11 2017-02-25 W B
我需要将其扩展/转换为以下内容,在开始和结束列之间填写数周(W-SAT)并转发填写< strong>注意和项目:
Start Note Item
2016-10-22 Z A
2016-10-29 Z A
2016-11-05 Z A
2017-02-11 W B
2017-02-18 W B
2017-02-25 W B
用熊猫做最好的方法是什么?某种多指数适用?
答案 0 :(得分:10)
您可以迭代每一行并创建一个新的数据框,然后将它们连接在一起
pd.concat([pd.DataFrame({'Start': pd.date_range(row.Start, row.End, freq='W-SAT'),
'Note': row.Note,
'Item': row.Item}, columns=['Start', 'Note', 'Item'])
for i, row in df.iterrows()], ignore_index=True)
Start Note Item
0 2016-10-22 Z A
1 2016-10-29 Z A
2 2016-11-05 Z A
3 2017-02-11 W B
4 2017-02-18 W B
5 2017-02-25 W B
答案 1 :(得分:1)
您根本不需要迭代。
df_start_end = df.melt(id_vars=['Note','Item'],value_name='date')
df = df_start_end.groupby('Note').apply(lambda x: x.set_index('date').resample('W').pad()).drop(columns=['Note','variable']).reset_index()
答案 2 :(得分:0)
如果df['End'] - df['Start']
的唯一值的数量不是太大,但是数据集中的行数很大,那么以下函数将比循环遍历数据集快得多。
def date_expander(dataframe: pd.DataFrame,
start_dt_colname: str,
end_dt_colname: str,
time_unit: str,
new_colname: str,
end_inclusive: bool) -> pd.DataFrame:
td = pd.Timedelta(1, time_unit)
# add a timediff column:
dataframe['_dt_diff'] = dataframe[end_dt_colname] - dataframe[start_dt_colname]
# get the maximum timediff:
max_diff = int((dataframe['_dt_diff'] / td).max())
# for each possible timediff, get the intermediate time-differences:
df_diffs = pd.concat([pd.DataFrame({'_to_add': np.arange(0, dt_diff + end_inclusive) * td}).assign(_dt_diff=dt_diff * td)
for dt_diff in range(max_diff + 1)])
# join to the original dataframe
data_expanded = dataframe.merge(df_diffs, on='_dt_diff')
# the new dt column is just start plus the intermediate diffs:
data_expanded[new_colname] = data_expanded[start_dt_colname] + data_expanded['_to_add']
# remove start-end cols, as well as temp cols used for calculations:
data_expanded = data_expanded.drop(columns=[start_dt_colname, end_dt_colname, '_to_add', '_dt_diff'])
# don't modify dataframe in place:
del dataframe['_dt_diff']
return data_expanded