如何使用计算值在 pandas 中创建新列并为每一行分配特定值？

Question

I have a dataframe df like

      Cards                   Height                    State Hash
0    1615427359331  ...         1945  3NK38AjJUMDcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...
0    1615427359331  ...         1952  3NK38AjJUMvcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...
0    1615427359331  ...         1958  3NK38AjJUMDhEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...
0    1615427359331  ...         1952  3NK38AjJUMDcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...
0    1615427359331  ...         1957  3NK38AjJUMhDcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT..

I have calculated the value of the most occuring State hash in the dataframe and the median of height

Using these two calculated values, how can I create a New column ON/OFF. and set the value as 1 or 0. Value 1 would be set if those two calculated values are matching else the value would be set to 0. How do I do that using Pandas dataframe? Please help. Thanks!

**EDIT: ** This is what ive tried so far

if [(dataframe['Block Height'] == median_highest_blocklen_recievd) & (dataframe['State Hash'] == most_commonrec_stat_hash)]:
    dataframe['online/offline'] = 1
if [(dataframe['Block Height'] != median_highest_blocklen_recievd) | (dataframe['State Hash'] != most_commonrec_stat_hash)]:
    dataframe['online/offline'] = -1

and This gave me the dataframe as

      Cards                   Height                    State Hash   Online/Offline
                                                      
0    1615427359331  ...         1945  3NK38AjJUMDcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...    -1
0    1615427359331  ...         1952  3NK38AjJUMvcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...    -1
0    1615427359331  ...         1958  3NK38AjJUMDhEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...    -1
0    1615427359331  ...         1952  3NK38AjJUMDcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT...    -1
0    1615427359331  ...         1957  3NK38AjJUMhDcEkPVaTWKsMcccUWXywgpMRseaNNPz6gyhT..    -1

Answer 1

I'm not too sure what your use case is, but you definitely want to use numpy's where

For example:

df['ON/OFF'] = np.where((df['Height']==median_height) & (df['State Hash']==mode_hash),1,0)

Answer 2

One approach is to do it with apply:

df['New'] = df.apply(lambda x: 1 if x['Height'] == median_height and x['State Hash'] == calculated_hash else 0, axis = 1)