Answers for "rename column old colnames to new colnames pandas"

rename columns pandas

df.rename(columns={'oldName1': 'newName1',
                   'oldName2': 'newName2'},
          inplace=True, errors='raise')
# Make sure you set inplace to True if you want the change
# to be applied to the dataframe

pandas rename column

df.rename(columns={"old_col1": "new_col1", "old_col2": "new_col2"}, inplace=True)

rename multiple pandas columns with list

for j in range(len(df.columns)):
    old = df.columns[j]
    new = new_columns[j]
    df = df.rename(columns = {old:new})

df change column names

df.rename(columns={"A": "a", "B": "b", "C": "c"},
errors="raise", inplace=True)

change name of column pandas

#df.rename() will only return a new df with the new headers
#df = df.rename() will change the heders of the current dataframe 
df = df.rename(columns={"old_col1": "new_col1", "old_col2": "new_col2"})

how to give name to column in pandas

>gapminder.rename(columns={'pop':'population',
                          'lifeExp':'life_exp',
                          'gdpPercap':'gdp_per_cap'}, 
                 inplace=True)
 
>print(gapminder.columns)
 
Index([u'country', u'year', u'population', u'continent', u'life_exp',
       u'gdp_per_cap'],
      dtype='object')
 
>gapminder.head(3)
 
       country  year  population continent  life_exp  gdp_per_cap
0  Afghanistan  1952     8425333      Asia    28.801   779.445314
1  Afghanistan  1957     9240934      Asia    30.332   820.853030
2  Afghanistan  1962    10267083      Asia    31.997   853.100710