How can I subtract from subsequent column in pandas?

How can I subtract from column before itself for many columns without hardcoding it? I can do it by hard coding it as shown below:

import pandas as pd
df = pd.DataFrame({"a":[1,2,3,4],"b":[1,3,5,6],"c":[6,7,8,9]})

df['a_diff'] = df['a']-16
df['b_diff'] = df['b']-df['a']
df['c_diff'] = df['c']-df['b']

I know there is a way to do it rowwise by using shift function. Can we also it as column wise? There are 100 columns I need to use this technique on so I would rather do it pythonically instead of hard coding it. Please note that "a_diff" was subtracted from constant intentionally since I will have to subtract that column by constant in my code as well.

Thank you,

Sam

Use diff and combine_first (or fillna with some limitations!) then rename with add_suffix and join to the original DataFrame:

out = df.join(df.diff(axis=1).combine_first(df[['a']].sub(16)).add_suffix('_diff'))

Or, if you are sure that there is no NaN in the columns other than "a":

out = df.join(df.diff(axis=1).fillna(df['a'].sub(16)).add_suffix('_diff'))

Output:

   a  b  c  a_diff  b_diff  c_diff
0  1  1  6   -15.0       0       5
1  2  3  7   -14.0       1       4
2  3  5  8   -13.0       2       3
3  4  6  9   -12.0       2       3

Use DataFrame.diff with set first column by DataFrame.fillna and DataFrame.add_suffix, last append to original by DataFrame.join:

df = df.join(df.diff(axis=1).fillna({'a': df['a'].sub(16)}).add_suffix('_diff'))

Solution without specify hardcoding first column:

first  = df.columns[0]
df = df.join(df.diff(axis=1).fillna({first: df[first].sub(16)}).add_suffix('_diff'))

Or set first column by difference:

df1 = df.diff(axis=1)
df1.iloc[:, 0] = df.iloc[:, 0].sub(16)
df = df.join(df1.add_suffix('_diff'))

Solution if not exist missing values in original DataFrame:

df = df.join(df.diff(axis=1).fillna(df.sub(16)).add_suffix('_diff'))

print (df)
   a  b  c  a_diff  b_diff  c_diff
0  1  1  6     -15       0       5
1  2  3  7     -14       1       4
2  3  5  8     -13       2       3
3  4  6  9     -12       2       3