To select only require column in pandas data frame, take note that the variable should be in list

df[['col1','col2']]

Result

To filter only require row in pandas data frame, "col1" is the column name defined and "0" is the index of the rows

df.col1[0]

To filter data using range, same result

df[(df['col1'] > 0) & (df['col1'] < 2)]

To filter data using list

df[df['col1'].isin([1])]

Result

To remove na from dataframe, regardless column

combined.dropna()

To remove na from single column

combined[~combined['col2'].isna()]

Result

To add a new row

df = df.append([{'col1':3,'col2':4,'newcolumn':'new add manually'}])

Result

To modify a column value, example replace first character found in string with new value

df['newcolumn'].map(lambda x: x.replace(x[0:1], '<<Replaced>>'))

Result

To remove duplicate

duplicate=pd.concat([left,left,right])
duplicate.drop_duplicates(subset=('<<column to be prioritize>>'))

Result