[python] Pandas DataFrame: replace all values in a column, based on condition

I have a simple DataFrame like the following:

I want to select all values from the 'First Season' column and replace those that are over 1990 by 1. In this example, only Baltimore Ravens would have the 1996 replaced by 1 (keeping the rest of the data intact).

I have used the following:

df.loc[(df['First Season'] > 1990)] = 1

But, it replaces all the values in that row by 1, and not just the values in the 'First Season' column.

How can I replace just the values from that column?

df['First Season'].loc[(df['First Season'] > 1990)] = 1

strange that nobody has this answer, the only missing part of your code is the ['First Season'] right after df and just remove your curly brackets inside.

df.loc[df['First season'] > 1990, 'First Season'] = 1

Explanation:

df.loc takes two arguments, 'row index' and 'column index'. We are checking if the value is greater than 1990 of each row value, under "First season" column and then we replacing it with 1.

A bit late to the party but still - I prefer using numpy where:

import numpy as np
df['First Season'] = np.where(df['First Season'] > 1990, 1, df['First Season'])

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)> ]

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']
   )

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 here is:

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

We can update the First Season column in df with the following syntax:

df['First Season'] = expression_for_new_values

To map the values in First Season we can use pandas‘ .map() method with the below syntax:

data_frame(['column']).map({'initial_value_1':'updated_value_1','initial_value_2':'updated_value_2'})

Another option is to use a list comprehension:

df['First Season'] = [1 if year > 1990 else year for year in df['First Season']]

df["First season"] = df["First season"].apply(lambda x : 1 if x > 1990 else x)