We are able to change certain features of our dataset from what it originally was through pandas.
Here are some of the ways we can do that:
1. Changing datatypes
2. Sorting the datatypes
Changing datatypes
If we have certain data that are not in our desired datatype we can simply reassign it as show below.
We make use of our basic pandas function, df.info(), to see the changes made.
"""We can change data types using astype as well"""
print(df.info())
df['net_sales'] = df['net_quantity'].astype('float64')
print(df.info())
Sorting the data
We can now make changes to the order of the dataset according to certain rules we want.
"""ascending=False as we want to to sort in descending order""" df.sort_values(by='net_sales', ascending=False).head() """we can sort to multiple and specific columns""" df.sort_values(by=['order_fufilled', 'net_sales'], ascending=[False, True]).head()
We can also acquire specific statistical information using common pandas syntaxes, as well as retrieve information with slicing methods similar to a list, try out these examples and take a look at the output.
1. Obtaining mean of net sales
df['net_sales'].mean()
2. Obtaining statistics of fufilled orders only (i.e. order_fulfilled==1)
df[df['order_fufilled'] == 1].mean()
3. Output the mean cost of fulfilled orders only
df[df['order_fufilled'] == 1]['cost_of_sales'].mean()
4. Acquiring maximum net sales of orders that weren't fufilled and placed before 1/1/2019
df[(df['order_fufilled'] == 0) & (df['date'] < "1/1/2019")]['net_sales'].max()
5. Data frame slicing rows 0-20 for columns net_sales to net_quantity
df.loc[0:20, 'net_sales':'net_quantity']
6. Data frame slicing rows 0 to 4 and columns 0 to 2 as indices
df.iloc[0:5, 0:3]
7. Calling last row, all columns of the data set
df[-1:]