For multiple conditions ie. (df['employrate'] <=55) & (df['employrate'] > 50)

use this:

df['employrate'] = np.where(
   (df['employrate'] <=55) & (df['employrate'] > 50) , 11, df['employrate']
   )

or you can do it this way as well,

gm.loc[(gm['employrate'] <55) & (gm['employrate'] > 50),'employrate']=11

here informal syntax can be:

<dataset>.loc[<filter1> & (<filter2>),'<variable>']='<value>'

out[108]:
       country  employrate alcconsumption
0  Afghanistan   55.700001            .03
1      Albania   11.000000           7.29
2      Algeria   11.000000            .69
3      Andorra         nan          10.17
4       Angola   75.699997           5.57

therefore syntax we used here is:

 df['<column_name>'] = np.where((<filter 1> ) & (<filter 2>) , <new value>, df['column_name'])

for single condition, ie. ( 'employrate'] > 70 )

       country        employrate alcconsumption
0  Afghanistan  55.7000007629394            .03
1      Albania  51.4000015258789           7.29
2      Algeria              50.5            .69
3      Andorra                            10.17
4       Angola  75.6999969482422           5.57

use this:

df.loc[df['employrate'] > 70, 'employrate'] = 7

       country  employrate alcconsumption
0  Afghanistan   55.700001            .03
1      Albania   51.400002           7.29
2      Algeria   50.500000            .69
3      Andorra         nan          10.17
4       Angola    7.000000           5.57

therefore syntax here is:

df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]

What I want to achieve: Condition: where column2 == 2 leave to be 2 if column1 < 30 elsif change to 3 if column1 > 90

This can be simplified into where (column2 == 2 and column1 > 90) set column2 to 3. The column1 < 30 part is redundant, since the value of column2 is only going to change from 2 to 3 if column1 > 90.

In the code that you provide, you are using pandas function replace, which operates on the entire Series, as stated in the reference:

Values of the Series are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

This means that for each iteration of for x in filter1 your code performs global replacement, which is not what you want to do - you want to update the specific row of column2 that corresponds to x from column1 (which you are iterating over).

the problem is 2 does not change to 3 where column1 > 90

This is truly strange. I would expect the code you provided to have changed every instance of 2 in column2 to 3 as soon as it encountered an x >= 30, as dictated by your code conditional statement (the execution of the else branch). This discrepancy may stem from the fact that you are assigning to column2 the result of global replacement performed on the column Output (the contents of which are unknown). In any case, if you want your program to do something under a specific condition, such as x > 90, it should be explicitly stated in the code. You should also note that the statement data['column2'] = data['column2'].replace([2], [2]) achieves nothing, since 2 is being replaced with 2 and the same column is both the source and the destination.

What you could use to solve this particular task is a boolean mask (or the query method). Both are explained in an excellent manner in this question.

Using a boolean mask would be the easiest approach in your case:

mask = (data['column2'] == 2) & (data['column1'] > 90)
data['column2'][mask] = 3

The first line builds a Series of booleans (True/False) that indicate whether the supplied condition is satisfied. The second line assigns the value 3 to those rows of column2 where the mask is True.

You are very close, but you have the arguments switched in np.where, the syntax is np.where(cond, if_cond_True, if_cond_False). The columns A and B should have the value of column if the condition is satisfied (if_cond_True), otherwise they keep their original values (if_cond_False).

import pandas as pd
import numpy as np 

data_in = {'A':['A1', '', '', 'A4',''],
        'B':['', 'B2', 'B3', '',''],
        'C':['C1','C2','','','C5']}

df_in = pd.DataFrame(data_in)

maskA = df_in['A'] != ''   # A not empty
maskB = df_in['B'] != ''   # B not empty
maskC = df_in['C'] != ''   # C not empty

# If the column havs NaNs instead of '' then use : 
#
# maskA = df_in['A'].notnull()   # A not empty
# maskB = df_in['B'].notnull()   # B not empty
# maskC = df_in['C'].notnull()   # C not empty

# If A and C are not empty, A = C, else A keep its value 
df_in['A'] = np.where(maskA & maskC, df_in['C'], df_in['A'])

# If B and C are not empty, B = C, else B keep its value
df_in['B'] = np.where(maskB & maskC, df_in['C'], df_in['B'])

# If (A and C are not empty) or (B and C are not empty),
# C should be empty, else C keep its value
df_in['C'] = np.where((maskA & maskC) | (maskB & maskC), "", df_in['C'])

Output

>>> df_in 

    A   B   C
0  C1        
1      C2    
2      B3    
3  A4        
4          C5

You need to select that column:

In [41]:
df.loc[df['First Season'] > 1990, 'First Season'] = 1
df

Out[41]:
                 Team  First Season  Total Games
0      Dallas Cowboys          1960          894
1       Chicago Bears          1920         1357
2   Green Bay Packers          1921         1339
3      Miami Dolphins          1966          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers          1950         1003

So the syntax here is:

df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]

You can check the docs and also the 10 minutes to pandas which shows the semantics

EDIT

If you want to generate a boolean indicator then you can just use the boolean condition to generate a boolean Series and cast the dtype to int this will convert True and False to 1 and 0 respectively:

In [43]:
df['First Season'] = (df['First Season'] > 1990).astype(int)
df

Out[43]:
                 Team  First Season  Total Games
0      Dallas Cowboys             0          894
1       Chicago Bears             0         1357
2   Green Bay Packers             0         1339
3      Miami Dolphins             0          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers             0         1003