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df['New Column'] = np.where(df['A']==0, df['B'], df['A'])
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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
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import pandas as pd
# Creating a sample dataframe
data = {'Name': ['John', 'Emma', 'Mike', 'Sophia'],
'Age': [25, 30, 35, 40],
'Salary': [50000, 60000, 45000, 70000]}
df = pd.DataFrame(data)
# Condition: Changing Salary to 55000 where Age is greater than 30
df.loc[df['Age'] > 30, 'Salary'] = 55000
# Print the updated DataFrame
print(df)
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### 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
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# 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')
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# replace() syntax
DataFrame.replace(to_replace="<the_value_you_want_to_replace>", value="<new_value_for_input>", inplace=False, limit=None, regex=False, method='pad')
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d.loc[d["conditionDisplayName"] == "Brand New", "conditionDisplayName"] = 6
d.loc[d["conditionDisplayName"] != "Brand New", "conditionDisplayName"] = 4
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df['column name'] = df['column name'].replace(['old value'], 'new value')