Useful Pandas Methods

Data Understanding

1. read_csv()
2. head()
3. tail()
4. info()
5. describe()
6. isnull(), isnull().sum()
7. duplicated(keep=False)

Data cleaning

8. dropna()
9. fillna()
10. drop_duplicates()
11. to_datetime()
    to_datetime() function converts an object to datetime format.

Data Analysis and Manipulation

12. value_counts()
    df[‘Education’].value_counts()
13. unique()
    df[‘Education’].unique()
14. nunique()
    df[‘Education’].nunique() Output = 5
15. sort_values()
    df.sort_values(by=’Income’, ascending=False)
16. query()
    query() method filters out the data frame by the condition we want.
    df.query(‘Income > 100000’) or df[df[‘Income’] > 100000]

17. groupby()

    df.groupby(‘Education’)[‘Income’].mean()

18. pivot_table()—pivot_table() method creates a useful pivot table for us.
    There are 4 arguments we should use as input: data, index, columns and values.
    By default, the method uses the mean as an aggregation function. We can also change it.

    pd.pivot_table(data = df, values= ‘Income’, index = ‘Education’, columns = ‘Marital_Status’)

19. apply()
    df[‘Response’].apply(lambda x : ‘Accepted’ if x == 1 else ‘Not Accepted’ )
20. replace()
    df[‘Marital_Status’].replace(to_replace=[‘Alone’,’Divorced’,’Widow’,’YOLO’,’Absurd’],value=’Single’)