PYTHON

pandas replace values in column based on condition

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

replace column values pandas

df['column'] = df['column'].str.replace(',','-')
df

pandas replace data in specific columns with specific values

### replace one value ###
df["column"].replace("US","UK") # you can also use numerical values
### replace more than one value ###
df["column"].replace(["man","woman","child"],[1,2,3]) # you can also use numerical values
#   man ==> 1
# woman ==> 2
# child ==> 3

change column value based on another column pandas

# Changes the 'is_electric' column based on value in the 'type' column
# If the 'type' column == 'electric' then the 'is_electric' becomes 'YES'
df['is_electric']= df['type'].apply(lambda x: 'YES' if (x == 'electric') else 'NO') 

replace values in a column by condition python

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

replace column values/create new column based on another column values/condition in Pandas

df['New Column'] = np.where(df['A']==0, df['B'], df['A'])

replace pandas column values based on condition

d.loc[d["conditionDisplayName"] == "Brand New", "conditionDisplayName"] = 6
d.loc[d["conditionDisplayName"] != "Brand New", "conditionDisplayName"] = 4