Create 2534 new columns in a dataframe based on the median values in the existing 2534 columns

I have a dataframe (df) similar to the one mentioned below:

I need a dataframe as mentioned below by looping through the code

Gene1_med = df['Gene1'].map(lambda exp: 'low' if exp <= df['Gene1'].median() else 'high')

I am new to python and any help is highly appreciated. Thanks in advance!

>Solution :

Use numpy.where with selecting columns of Genes, compare by DataFrame.le and assign changed columns names, last sorting columns:

cols = ['Gene1','Gene2','Gene3']

#columns PATID to index
df = df.set_index('PATID')

df[pd.Index(cols) + '_med'] = np.where(df[cols].le(df[cols].median()), 'low','high')

df1 = df.sort_index(axis=1).reset_index()

Or seelct all columns with substring Gene by DataFrame.filter, last sorting all columns without first and append to first column:

df1 = df.filter(like='Gene')

df[df1.columns + '_med'] = np.where(df1.le(df1.median()), 'low','high')

df1 = df.iloc[:, :1].join(df.iloc[:, 1:].sort_index(axis=1))

Or use list for columns names:

cols = ['Gene1','Gene2','Gene3']

df[pd.Index(cols) + '_med'] = np.where(df[cols].le(df[cols].median()), 'low','high')

df1 = df.iloc[:, :1].join(df.iloc[:, 1:].sort_index(axis=1))

print (df1)

   PATID  Gene1 Gene1_med  Gene2 Gene2_med  Gene3 Gene3_med
0   1001  5.899       low  67.87      high   9.87       low
1   1002  6.899       low  60.87       low  10.87      high
2   1003  7.899      high  63.87       low   8.87       low