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
