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'])

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.

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']]

You can also use mask which replaces the values where the condition is met:

df['First Season'].mask(lambda col: col > 1990, 1)

TLDR — Here is some usage guidance, including some methods that haven’t been mentioned yet:

1. Speed

Use DataFrame.loc if you have a large dataframe and are concerned about speed:

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

For small dataframes, speed is trivial, but technically there are faster options if you want:

^{df = pd.DataFrame({'Team': np.random.choice([*'ABCDEFG'], size=n), 'Season': np.random.randint(1900, 2001, size=n), 'Games': np.random.randint(0, 17, size=n)})}

2. Method chaining

Use a Series method if you want to conditionally replace values within a method chain:

• Series.mask replaces values where the given condition is true

  df['Season'] = df['Season'].mask(df['Season'] > 1990, 1)
  

• Series.where is just the inverted version (replace when false)

  df['Season'] = df['Season'].where(df['Season'] <= 1990, 1)
  

The chaining benefit is not obvious in OP’s example but is very useful in other situations. Just as a toy example:

# compute average games per team, but pre-1972 games are weighted by half
df['Games'].mask(df['Season'] < 1972, 0.5*df['Games']).groupby(df['Team']).mean()

Practical examples:

• mask certain values to 0 before grouping
• mask certain values as another column before filling
• select where a condition occurs before grouping

3. Whole dataframe

Use DataFrame.mask if you want to conditionally replace values throughout the whole dataframe.

It’s not easy to come up with a meaningful example given OP’s sample, but here is a trivial example for demonstration:

# replace the given elements with the doubled value (or repeated string)
df.mask(df.isin(['Chicago Bears', 'Buffalo Bills', 8, 1990]), 2*df)

Practical example:

• mask each row’s maximum with its mean

4. Multiple conditions

Use np.select if you have multiple conditions, each with a different replacement:

# replace pre-1920 seasons with 0 and post-1990 seasons with 1
conditions = {
    0: df['Season'] < 1920,
    1: df['Season'] > 1990,
}
df['Season'] = np.select(conditions.values(), conditions.keys(), default=df['Season'])

Practical example:

• replace values based on multiple regex conditions