Dealing With Pivot Tables

Python has an analog of the .groupby() method that can lead to the same result. It is up to you as to which function to use. Let's learn it by using an example. Using the following function, called .pivot_table(), we will calculate the mean values of the column 'Length' that have the same value in the column 'Flight':


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import pandas as pd

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane', index_col = 0)

# The code using .groupby()
data_flights_1 = data[['Length', 'Flight']].groupby('Flight').mean()
# The same code using .groupby()
data_flights_2 = data[['Length', 'Flight']].groupby('Flight').agg('mean')

# The same code using .pivot_table()
data_flights_3 = pd.pivot_table(data, values = 'Length',
                      index = 'Flight',
                      aggfunc = 'mean')

print(data_flights_1.head())

Explanation:

data = pd.pivot_table(data, values = 'Length',
                      index = 'Flight',
                      aggfunc = 'mean')

pd.pivot_table() - function that creates pivot tables;
data - data frame that we use;
values = 'Length' - to the argument values, we assign columns having the same group, for which we will apply the calculation of the average, maximum, etc. If you want to group by several columns, put them in the list; the order isn't crucial;
index = 'Flight' - index is an argument to which you assign the name of a column or columns that you want to group. If you want to group by several columns, put them in the list; the order is crucial, like in the .groupby() function;
aggfunc = 'mean' - the same as agg in the .groupby() method, aggfunc has exactly the same syntax as agg. Thus, you can put several functions here by putting them in the list to specify functions for different columns using curly brackets.

Here, you have the example of a code that uses the .groupby() statement. Please choose the analog using the .pivot_table() function.

Select the correct answer

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane', index_col = 0)data_flights = pd.pivot_table(data, values = ['AirportFrom', 'AirportTo'], index = ['Length', 'Time'], aggfunc = 'min')print(data_flights.head())

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane', index_col = 0)data_flights = pd.pivot_table(data, values = ['AirportTo', 'AirportFrom'], index = ['Length', 'Time'], aggfunc = 'mean')print(data_flights.head())

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane', index_col = 0)data_flights = pd.pivot_table(data, values = 'AirportTo', 'AirportFrom', index = ['Length', 'Time'], aggfunc = 'min')print(data_flights.head())

Var allt tydligt?

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Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

2. Dealing With Conditions

Setting Condition Selecting Data Based on Condition Dealing With Several Conditions Making Your Code Beautiful Conditions Quiz

3. Extracting Data

Is Data in ...?Combining Your Knowledge Between Method Extracting Specific Data Finding the Smallest Values of a Column Finding the Largest Values of a Column Finding the Correlation

4. Aggregating Data

Getting Familiar With the .groupby() Method Grouping by Several Columns Complicated Grouping Advanced Grouping Dealing With Pivot Tables Creating a Pivot Table

5. Preprocessing Data