Author: John Au-Yeung

To convert a Python Pandas GroupBy output from Series to DataFrame, we can use count.

For instance, we write

df1.groupby(["Name", "City"])[['Name','City']].count()

to call groupby with count to return the groupby result as a data frame.

To convert Python Pandas dataframe to NumPy array, we can use the to_numpy method.

For instance, we write

df = pd.DataFrame(data={'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}, 
                  index=['a', 'b', 'c'])
n = df.to_numpy()

to create the df data frame with some data in it.

Then we call df.to_numpy to return the df data frame as a NumPy object.

To get statistics for each group using Python Pandas GroupBy, we can call the size method.

For instance, we write

df.groupby(['col1', 'col2']).size().reset_index(name='counts')

to call groupby with an array of columns.

Then we call size to get the row counts.

And then we call reset_index to return the values in a data a frame in the 'counts' column.

To set value for particular cell in Python Pandas DataFrame using index, we can use the set_value method.

For instance, we write

df.set_value('C', 'x', 10)

to set the value of the 'C‘ column in 'x' row of the df data frame to 10.

To filter Python Pandas dataframe using ‘in’ and ‘not in’ like in SQL, we call the isin method.

For instance, we write

df[df.country.isin(countries_to_keep)]

to call df.country.isin to get the rows that has the country column set to the values in the countries_to_keep list.

We can negate isin with ~, so we can write

df[~df.country.isin(countries_to_keep)]

to call df.country.isin to get the rows that has the country column that aren’t set to the values in the countries_to_keep list.