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Pivot Tables

It's time to deal with a similar function called .pivot_table(). Indeed, it is very similar to .groupby(), but the syntax is different. Here using agg functions is obligatory. If you remember, several chapters ago, we were working with this dataset:

And this example:


              1234567
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/INTRO+to+Python/ds_salaries.csv', index_col = 0)

df = df[['salary','job_title', 'experience_level']].groupby(['job_title', 'experience_level']).mean()

print(df)

Look at the result:

Let's practice, look at the implimentation using .pivot_table() to get the same result:


              12345678910
            
import pandas as pd
import numpy as np

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/INTRO+to+Python/ds_salaries.csv', index_col = 0)

df = pd.pivot_table(df, index = ['plan','trial'],
                       values = ['price'],
                       aggfunc = [np.mean])

print(df)

You should put the dataset as the first argument.
Put columns on which you want to group the data to the array index ; the order is crucial, like in .groupby() .
Put columns you want to group to the array values (to calculate mean, median, etc.). The order is not crucial. Indeed, this argument is not obligatory; otherwise, aggfunc will be applied for all numerical columns depending on their group.
Put NumPy functions that you want to apply to grouped columns to the array aggfunc (to calculate mean, median, etc.); the order is not crucial. One of those that we learned. But use them without brackets and arguments, just the function's title like np.mean() or np.sum() .

Tâche

Swipe to start coding

Your task is to create a pivot table where you will group by plan and count mean and median price. Check, if they vary. Follow the algorithm:

Create a pivot table with the arguments:

df as the first argument.
'plan' to the index as the second argument.
'price' to the values as the second argument.
np.mean and np.median to the aggfunc as the third argument.

Print the df.

By the way, if they vary significantly, you have outliers (incredibly small or big values).

Solution

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Merci pour vos commentaires !

Section 3. Chapitre 11

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Pivot Tables

And this example:


              1234567
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/INTRO+to+Python/ds_salaries.csv', index_col = 0)

df = df[['salary','job_title', 'experience_level']].groupby(['job_title', 'experience_level']).mean()

print(df)

Look at the result:

Let's practice, look at the implimentation using .pivot_table() to get the same result:


              12345678910
            
import pandas as pd
import numpy as np

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/INTRO+to+Python/ds_salaries.csv', index_col = 0)

df = pd.pivot_table(df, index = ['plan','trial'],
                       values = ['price'],
                       aggfunc = [np.mean])

print(df)

You should put the dataset as the first argument.
Put columns on which you want to group the data to the array index ; the order is crucial, like in .groupby() .
Put columns you want to group to the array values (to calculate mean, median, etc.). The order is not crucial. Indeed, this argument is not obligatory; otherwise, aggfunc will be applied for all numerical columns depending on their group.
Put NumPy functions that you want to apply to grouped columns to the array aggfunc (to calculate mean, median, etc.); the order is not crucial. One of those that we learned. But use them without brackets and arguments, just the function's title like np.mean() or np.sum() .

Tâche

Swipe to start coding

Your task is to create a pivot table where you will group by plan and count mean and median price. Check, if they vary. Follow the algorithm:

Create a pivot table with the arguments:

df as the first argument.
'plan' to the index as the second argument.
'price' to the values as the second argument.
np.mean and np.median to the aggfunc as the third argument.

Print the df.

By the way, if they vary significantly, you have outliers (incredibly small or big values).

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 11

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous