Apply number ceiling/threshold to pandas dataframe

Use:

df = df.mask(df.gt(2.5).cumsum(1).gt(0), 3)#same as#df = df.mask((df > 2.5).cumsum(axis=1) > 0, 3)print (df)         1    2    3    4    5    6    7John   1.3  3.0  3.0  3.0  3.0  3.0  3.0Terry  1.1  2.3  3.0  3.0  3.0  3.0  3.0Henry  0.3  1.0  2.0  3.0  3.0  3.0  3.0

Detail:

First compare by 2.5 all values by gt:

print (df.gt(2.5))           1      2      3     4     5      6     7John   False   True   True  True  True   True  TrueTerry  False  False   True  True  True  False  TrueHenry  False  False  False  True  True  False  True

Then get cumsum by columns by axis=1:

print (df.gt(2.5).cumsum(axis=1))       1  2  3  4  5  6  7John   0  1  2  3  4  5  6Terry  0  0  1  2  3  3  4Henry  0  0  0  1  2  2  3

And compare with 0 by eq:

print (df.gt(2.5).cumsum(axis=1).gt(0))           1      2      3     4     5     6     7John   False   True   True  True  True  True  TrueTerry  False  False   True  True  True  True  TrueHenry  False  False  False  True  True  True  True

Last replace Trues by 3 by mask:

print (df.mask(df.gt(2.5).cumsum(1).gt(0), 3))         1    2    3    4    5    6    7John   1.3  3.0  3.0  3.0  3.0  3.0  3.0Terry  1.1  2.3  3.0  3.0  3.0  3.0  3.0Henry  0.3  1.0  2.0  3.0  3.0  3.0  3.0 

For improve performance is possible use numpy:

a = df.valuesdf1 = pd.DataFrame(np.where(np.cumsum(a > 2.5, axis=1) > 0, 3, a),                    index=df.index,                   columns=df.columns)print (df1)         1    2    3    4    5    6    7John   1.3  3.0  3.0  3.0  3.0  3.0  3.0Terry  1.1  2.3  3.0  3.0  3.0  3.0  3.0Henry  0.3  1.0  2.0  3.0  3.0  3.0  3.0

We can using idxmax

s=df.gt(2.5).idxmax(1)for x in list(range(len(s))):    df.loc[s.index[x],s[x]:]=3dfOut[585]:          1    2    3    4    5    6    7John   1.3  3.0  3.0  3.0  3.0  3.0  3.0Terry  1.1  2.3  3.0  3.0  3.0  3.0  3.0Henry  0.3  1.0  2.0  3.0  3.0  3.0  3.0