Contenido del Curso

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

Outputting Columns by Title Outputting Rows by Index Selecting Specific Rows and Columns Learning More About Indexation Getting Familiar With lambda Functions Expanding Functionality of the .iloc[] Attribute

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

Checking for Missing Values Calculating the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing Values Managing Categorical Variables Checking the Column Type Managing an Incorrect Column Renaming the Column

Complicated Grouping

It is sometimes the case that we aren't satisfied with built-in pandas functions, like .mean() or .min() while grouping.

Look at the column 'Length'; here, we have the flight length in minutes. Imagine we want to calculate the maximum time in hours for items having the same value in the 'Flight' column and then in the 'Airline' one. To do so, we can calculate the maximum value of the column 'Length' for each group key and then divide it by 60. Look at the example and the explanation below.


              1234
            
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)
data_flights = data[['Flight', 'Airline', 'Length']].groupby(['Flight', 'Airline']).apply(lambda x: x['Length'].max()/60)
print(data_flights.head(10))

Explanation:

We made the example from the previous chapters a little bit complicated, so with data grouping, everything is the same; let's turn to the .apply() method.

.apply() - it helps apply specific function to the needed columns;
in the lambda function, x is the argument and x['Length'].max()/60 is the expression. So, the function finds the maximum value for each group key and divides the aggregated value by 60.

Tarea

Your task here is to group data by the airport from which the flight started and then by the weekday. Calculate the minimum amount of time of the sum of the groups' columns 'Length' and 'Time' for the groups to figure out how long the flight with delay may take. Follow the algorithm to manage the task:

Group data:

Store the list of columns 'AirportFrom', 'Airline', 'Time', and 'Length' (in this order) in the columns variable;
Extract columns from data;
The order is crucial within the .groupby() method; put the columns 'AirportFrom' and 'Airline' in this order;
Apply the function to the values of the data set having the same group keys;
Calculate the sum of two columns: 'Length' and 'Time'. Then find their minimum.

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¿Todo estuvo claro?

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Sección 4. Capítulo 3

Complicated Grouping

It is sometimes the case that we aren't satisfied with built-in pandas functions, like .mean() or .min() while grouping.


              1234
            
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)
data_flights = data[['Flight', 'Airline', 'Length']].groupby(['Flight', 'Airline']).apply(lambda x: x['Length'].max()/60)
print(data_flights.head(10))

Explanation:

We made the example from the previous chapters a little bit complicated, so with data grouping, everything is the same; let's turn to the .apply() method.

.apply() - it helps apply specific function to the needed columns;
in the lambda function, x is the argument and x['Length'].max()/60 is the expression. So, the function finds the maximum value for each group key and divides the aggregated value by 60.

Tarea

Group data:

Store the list of columns 'AirportFrom', 'Airline', 'Time', and 'Length' (in this order) in the columns variable;
Extract columns from data;
The order is crucial within the .groupby() method; put the columns 'AirportFrom' and 'Airline' in this order;
Apply the function to the values of the data set having the same group keys;
Calculate the sum of two columns: 'Length' and 'Time'. Then find their minimum.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 4. Capítulo 3

Complicated Grouping

It is sometimes the case that we aren't satisfied with built-in pandas functions, like .mean() or .min() while grouping.


              1234
            
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)
data_flights = data[['Flight', 'Airline', 'Length']].groupby(['Flight', 'Airline']).apply(lambda x: x['Length'].max()/60)
print(data_flights.head(10))

Explanation:

We made the example from the previous chapters a little bit complicated, so with data grouping, everything is the same; let's turn to the .apply() method.

.apply() - it helps apply specific function to the needed columns;
in the lambda function, x is the argument and x['Length'].max()/60 is the expression. So, the function finds the maximum value for each group key and divides the aggregated value by 60.

Tarea

Group data:

Store the list of columns 'AirportFrom', 'Airline', 'Time', and 'Length' (in this order) in the columns variable;
Extract columns from data;
The order is crucial within the .groupby() method; put the columns 'AirportFrom' and 'Airline' in this order;
Apply the function to the values of the data set having the same group keys;
Calculate the sum of two columns: 'Length' and 'Time'. Then find their minimum.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

It is sometimes the case that we aren't satisfied with built-in pandas functions, like .mean() or .min() while grouping.


              1234
            
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)
data_flights = data[['Flight', 'Airline', 'Length']].groupby(['Flight', 'Airline']).apply(lambda x: x['Length'].max()/60)
print(data_flights.head(10))

Explanation:

We made the example from the previous chapters a little bit complicated, so with data grouping, everything is the same; let's turn to the .apply() method.

.apply() - it helps apply specific function to the needed columns;
in the lambda function, x is the argument and x['Length'].max()/60 is the expression. So, the function finds the maximum value for each group key and divides the aggregated value by 60.

Tarea

Group data:

Store the list of columns 'AirportFrom', 'Airline', 'Time', and 'Length' (in this order) in the columns variable;
Extract columns from data;
The order is crucial within the .groupby() method; put the columns 'AirportFrom' and 'Airline' in this order;
Apply the function to the values of the data set having the same group keys;
Calculate the sum of two columns: 'Length' and 'Time'. Then find their minimum.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 4. Capítulo 3

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones