CODEWITHSUNDEEP
pythonpandasdataframemerge
Emerson
Emerson

Reputation: 125

Compare two different dataframes based on multiple row conditions

I have two dataframes containing different information about the same patients. I need to use dataframe 1 to filter dataframe 2 so that dataframe 2 will only keep its integer patient row values if there is an integer value in df_1 for the same chromosome, strand, elementloc, and patient. If there is an NaN value in df_1, I'd like to put NaN in df_2 in that same location. For NaN values already in df_2, I'd like to leave them as NaN.

So with df_1 and df_2 like:

df_1 = pd.DataFrame({'chromosome': [1, 1, 5, 4],
                     'strand': ['-', '-', '+', '-'],
                     'elementloc': [4991, 8870, 2703, 9674],
                     'Patient1_Reads': ['NaN', 25, 50, 'NaN'],
                     'Patient2_Reads': [35, 200, 'NaN', 500]})

print(df_1)                                                                    
   chromosome strand  elementloc Patient1_Reads Patient2_Reads
0           1      -        4991            NaN             35
1           1      -        8870             25            200
2           5      +        2703             50            NaN
3           4      -        9674            NaN            500


df_2 = pd.DataFrame({'chromosome': [1, 1, 5, 4],
                     'strand': ['-', '-', '+', '-'],
                     'elementloc': [4991, 8870, 2703, 9674],
                     'Patient1_PSI': [0.76, 0.35, 0.04, 'NaN'],
                     'Patient2_PSI': [0.89, 0.15, 0.47, 0.32]})
print(df_2)                                                                      
   chromosome strand  elementloc   Patient1_PSI    Patient2_PSI
0           1      -        4991           0.76            0.89
1           1      -        8870           0.35            0.15
2           5      +        2703           0.04            0.47
3           4      -        9674            NaN            0.32

I would like new df_2 to look like:

   chromosome strand  elementloc  Patient1_PSI  Patient2_PSI
0           1      -        4991           NaN          0.89
1           1      -        8870          0.35          0.15
2           5      +        2703          0.04           NaN
3           4      -        9674           NaN          0.32

Upvotes: 2

Views: 47

Answers (1)

Shubham Sharma
Shubham Sharma

Reputation: 71689

Use:

df3 = df1.merge(df2, on=['chromosome', 'strand', 'elementloc'])

r_cols = df3.columns[df3.columns.str.endswith('_Reads')]
p_cols = r_cols.str.strip('Reads') + 'PSI'

df3[p_cols] = df3[p_cols].mask(df3[r_cols].isna().to_numpy())
df3 = df3.drop(r_cols, 1)

Details:

STEP A: Use DataFrame.merge to create a merged dataframe df3 obtained by merging the dataframes df1 and df2 on ['chromosome', 'strand', 'elementloc'].

# print(df3)
   chromosome strand  elementloc  Patient1_Reads  Patient2_Reads  Patient1_PSI  Patient2_PSI
0           1      -        4991             NaN            35.0          0.76          0.89
1           1      -        8870            25.0           200.0          0.35          0.15
2           5      +        2703            50.0             NaN          0.04          0.47
3           4      -        9674             NaN           500.0           NaN          0.32

STEP B: Use .str.endswith to get the columns in df3 which ends with _Reads we call this columns r_cols, then use this _Reads columns to obtain the corresponding _PSI columns we call this columns p_cols.

# print(r_cols)
Index(['Patient1_Reads', 'Patient2_Reads'], dtype='object')

# print(p_cols)
Index(['Patient1_PSI', 'Patient2_PSI'], dtype='object')

STEP C: Use DataFrame.isna on the _Reads columns to obtain the boolean mask, then use this mask along with DataFrame.mask to fill the correponding NaN values in _PSI columns. Finally use DataFrame.drop to drop the _Reads column from the merged datframe df3 to get the desired result:

# print(df3)
   chromosome strand  elementloc  Patient1_PSI  Patient2_PSI
0           1      -        4991           NaN          0.89
1           1      -        8870          0.35          0.15
2           5      +        2703          0.04           NaN
3           4      -        9674           NaN          0.32

Upvotes: 2

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