Assign True/False/NaN if any dataframe column is True/False/NaN
I have a dataframe containing appartment information on a room level. I would like to aggregate this information to an appartment level by combining similar columns into a single column. E.g. columns 'livingroom_tv' (bool) and 'bedroom_tv' (bool) should become a new column 'tv' (bool) which shows whether there is a tv in any of the other rooms (True/False) or no information (NaN). I have used the following loop:
for idx, values in df.iterrows():
df_temp=values
df_filter=df_temp.filter(like="tv",axis=0)
if df_filter.any():
df.at[idx,'tv'] = True
However, it can currently only differentiate between True and NaN. How can I achieve a 'False' statement in df['tv'] in case all other tv columns are False without messing with the NaN values (which would happen if I use else:)?
python pandas loops any
asked Nov 22 '18 at 23:15
HannahKorts
495
add a comment |
I have a dataframe containing appartment information on a room level. I would like to aggregate this information to an appartment level by combining similar columns into a single column. E.g. columns 'livingroom_tv' (bool) and 'bedroom_tv' (bool) should become a new column 'tv' (bool) which shows whether there is a tv in any of the other rooms (True/False) or no information (NaN). I have used the following loop:
for idx, values in df.iterrows():
df_temp=values
df_filter=df_temp.filter(like="tv",axis=0)
if df_filter.any():
df.at[idx,'tv'] = True
However, it can currently only differentiate between True and NaN. How can I achieve a 'False' statement in df['tv'] in case all other tv columns are False without messing with the NaN values (which would happen if I use else:)?
python pandas loops any
asked Nov 22 '18 at 23:15
HannahKorts
495
1
Please post sample of rows with desired result for us to help. Thanks!
– Parfait
Nov 23 '18 at 1:28
add a comment |
I have a dataframe containing appartment information on a room level. I would like to aggregate this information to an appartment level by combining similar columns into a single column. E.g. columns 'livingroom_tv' (bool) and 'bedroom_tv' (bool) should become a new column 'tv' (bool) which shows whether there is a tv in any of the other rooms (True/False) or no information (NaN). I have used the following loop:
for idx, values in df.iterrows():
df_temp=values
df_filter=df_temp.filter(like="tv",axis=0)
if df_filter.any():
df.at[idx,'tv'] = True
However, it can currently only differentiate between True and NaN. How can I achieve a 'False' statement in df['tv'] in case all other tv columns are False without messing with the NaN values (which would happen if I use else:)?
python pandas loops any
asked Nov 22 '18 at 23:15
HannahKorts
495
I have a dataframe containing appartment information on a room level. I would like to aggregate this information to an appartment level by combining similar columns into a single column. E.g. columns 'livingroom_tv' (bool) and 'bedroom_tv' (bool) should become a new column 'tv' (bool) which shows whether there is a tv in any of the other rooms (True/False) or no information (NaN). I have used the following loop:
for idx, values in df.iterrows():
df_temp=values
df_filter=df_temp.filter(like="tv",axis=0)
if df_filter.any():
df.at[idx,'tv'] = True
However, it can currently only differentiate between True and NaN. How can I achieve a 'False' statement in df['tv'] in case all other tv columns are False without messing with the NaN values (which would happen if I use else:)?
python pandas loops any
python pandas loops any
asked Nov 22 '18 at 23:15
HannahKorts
495
asked Nov 22 '18 at 23:15
HannahKorts
495
asked Nov 22 '18 at 23:15
HannahKorts
495
asked Nov 22 '18 at 23:15
HannahKorts
495
asked Nov 22 '18 at 23:15
HannahKorts
495
495
1
Please post sample of rows with desired result for us to help. Thanks!
– Parfait
Nov 23 '18 at 1:28
add a comment |
1
Please post sample of rows with desired result for us to help. Thanks!
– Parfait
Nov 23 '18 at 1:28
1
1
Please post sample of rows with desired result for us to help. Thanks!
– Parfait
Nov 23 '18 at 1:28
Please post sample of rows with desired result for us to help. Thanks!
– Parfait
Nov 23 '18 at 1:28
add a comment |
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1
Please post sample of rows with desired result for us to help. Thanks!
– Parfait
Nov 23 '18 at 1:28