Contenido del Curso

Introduction to Python for Data Analysis

1. Introduction to Python 1/2

First Step in Programming Deal with Variables Practice Math Skills Challenge Deal with Percents How to Attract One User?Challenge Learn Numbers Boolean Numbers Challenge Work with Strings Get Familiar with Lists How to Append New Element?

2. Introduction to Python 2/2

If/Else Challenge If/Elif/Else Challenge For Loop While Loop Challenge

3. Explore Dataset

Data Study Operations with the File How to Explore the Data Sorting Data Max, min, mean, median Challenge Group Data Dive Deeper into Grouping Data Group Data 2.0 Introduction to NumPy Pivot Tables Recall Loops .loc Function Deal with Several Conditions Challenge Visualization: First Steps Dive Deeper into Visualization

4. Becoming an Analyst

Data Study Examine Dataset Challenge СAC 1/2 CAC 2/2 Costly Customers Organic Traffic How Much Do We Earn Is Our Project Profitable? ROI 1/2 ROI 2/2 Visualize ROI

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:

Look at the result:

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

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().

Tarea

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).

Tarea

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).

¿Todo estuvo claro?

Sección 3. Capítulo 11

Pivot Tables

And this example:

Look at the result:

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

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().

Tarea

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).

Tarea

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).

¿Todo estuvo claro?

Sección 3. Capítulo 11

Pivot Tables

And this example:

Look at the result:

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

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().

Tarea

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).

Tarea

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).

¿Todo estuvo claro?

And this example:

Look at the result:

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

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().

Tarea

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).

Sección 3. Capítulo 11

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