ax = sns.barplot(x="lovely_brand", y="user_id", data=brands_loyalty) barplot

dropna() for dropping null values

df[
~(df['workingday'] == 0)
&(df['temp'] >= 10)
&(df['temp'] < 30)
] with working filtering

pd.set_option('display.max_rows', 85, index_col ="Column") for showing out the all indexes in table

df['ConvertedCp'].nlargest() finding out the max()

df.sort_values(by=['Country', 'ConvertedCp,'], ascending=[True, False], inplace = True) sorting advanced

df.drop(index = 4)

deleting with index

country_grp['Salary'].median().loc['Germany'] Germany country looking for the Salary

country_uses_python = country_grp['LanguageWorkedWith'].apply(lambda x: x.str.contains('Python').sum())

where Python word contains

df.columns = df.columns.str.replace('', '_')

df.columns = [x.upper() for x in df.columns]

df.apply(len, axis='columns')


df['email'].apply(len)

len(df['email'])

df['full_name'] = df['first'] + '' + df['last']

adding columsn

df.drop(columns = ['first', ' last'], inplace = True)

removing column

df[['first', 'last']] = df['full_name'].str.split(' ', expand = True)

mergin two columns as one

df.dropna(axis ='index', how ='all', subset=['last', 'email'])


droping null values with specfiying indexes

df['age'] = df['age'].astype(float)


working with missing values

df['YearsCode'].unique()

getting only unique values