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Advanced Techniques in pandas
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Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
Outputting Columns by TitleOutputting Rows by IndexSelecting Specific Rows and ColumnsLearning More About IndexationGetting Familiar With lambda FunctionsExpanding Functionality of the .iloc[] Attribute
2. Dealing With Conditions
Setting ConditionSelecting Data Based on ConditionDealing With Several ConditionsMaking Your Code BeautifulConditions Quiz
3. Extracting Data
Is Data in ...?Combining Your KnowledgeBetween MethodExtracting Specific DataFinding the Smallest Values of a ColumnFinding the Largest Values of a ColumnFinding the Correlation
4. Aggregating Data
Getting Familiar With the .groupby() MethodGrouping by Several ColumnsComplicated GroupingAdvanced GroupingDealing With Pivot TablesCreating a Pivot Table
5. Preprocessing Data
Checking for Missing ValuesCalculating the Number of Missing ValuesWhat Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing ValuesManaging Categorical VariablesChecking the Column TypeManaging an Incorrect ColumnRenaming the Column

bookGrouping by Several Columns

Let's add some information on the .groupby() method. You can group by several columns, but the order is crucial in this case. In the previous chapter, we grouped data by the flight number and counted the number of delays. We can make this task complicated by grouping not only by the 'Flight' column, but also by the column 'Airline'. Refresh the information on the dataset and then look at this simple example (the output contains only the first 10 rows):


              
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', 'Delay', 'Airline']].groupby(['Flight', 'Airline']).count()
print(data_flights.head(10))
copy

Explanation:

  • data[['Flight', 'Delay', 'Airline']] - columns you will work with, including the columns by which you will group;
  • .groupby(['Flight', 'Airline']) - here, 'Flight' and 'Airline' are arguments of the function .groupby().

Pay attention; if you want to group by several columns, put them into the list - the order is crucial. So, in our case, if rows of the data set have the same value in the column 'Flight', they will relate to one group. Then inside those groups, the function finds other groups for rows with the same value in the column 'Airline'. Then, due to the method .count() that counts the rows, our function will calculate the number of rows in the column 'Delay' that have the same value in the column 'Airline' for each 'Flight' group.

Tarefa

Your task here is to group data by the airport from which the flight started and then by the weekday. Calculate the average time for the groups. Follow the algorithm to manage the task:

  1. Group data:
    • Extract the columns 'AirportFrom', 'DayOfWeek', and 'Time' from data (in this order);
    • Apply the .groupby() method to the previous columns;
    • Within the .groupby() method, put the columns 'AirportFrom' and 'DayOfWeek'; the order is crucial;
    • Calculate the mean value of the column 'Time'.
  2. Output the first 10 rows of the data_flights.

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Seção 4. Capítulo 2
toggle bottom row

bookGrouping by Several Columns

Let's add some information on the .groupby() method. You can group by several columns, but the order is crucial in this case. In the previous chapter, we grouped data by the flight number and counted the number of delays. We can make this task complicated by grouping not only by the 'Flight' column, but also by the column 'Airline'. Refresh the information on the dataset and then look at this simple example (the output contains only the first 10 rows):


              
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', 'Delay', 'Airline']].groupby(['Flight', 'Airline']).count()
print(data_flights.head(10))
copy

Explanation:

  • data[['Flight', 'Delay', 'Airline']] - columns you will work with, including the columns by which you will group;
  • .groupby(['Flight', 'Airline']) - here, 'Flight' and 'Airline' are arguments of the function .groupby().

Pay attention; if you want to group by several columns, put them into the list - the order is crucial. So, in our case, if rows of the data set have the same value in the column 'Flight', they will relate to one group. Then inside those groups, the function finds other groups for rows with the same value in the column 'Airline'. Then, due to the method .count() that counts the rows, our function will calculate the number of rows in the column 'Delay' that have the same value in the column 'Airline' for each 'Flight' group.

Tarefa

Your task here is to group data by the airport from which the flight started and then by the weekday. Calculate the average time for the groups. Follow the algorithm to manage the task:

  1. Group data:
    • Extract the columns 'AirportFrom', 'DayOfWeek', and 'Time' from data (in this order);
    • Apply the .groupby() method to the previous columns;
    • Within the .groupby() method, put the columns 'AirportFrom' and 'DayOfWeek'; the order is crucial;
    • Calculate the mean value of the column 'Time'.
  2. Output the first 10 rows of the data_flights.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 4. Capítulo 2
toggle bottom row

bookGrouping by Several Columns

Let's add some information on the .groupby() method. You can group by several columns, but the order is crucial in this case. In the previous chapter, we grouped data by the flight number and counted the number of delays. We can make this task complicated by grouping not only by the 'Flight' column, but also by the column 'Airline'. Refresh the information on the dataset and then look at this simple example (the output contains only the first 10 rows):


              
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', 'Delay', 'Airline']].groupby(['Flight', 'Airline']).count()
print(data_flights.head(10))
copy

Explanation:

  • data[['Flight', 'Delay', 'Airline']] - columns you will work with, including the columns by which you will group;
  • .groupby(['Flight', 'Airline']) - here, 'Flight' and 'Airline' are arguments of the function .groupby().

Pay attention; if you want to group by several columns, put them into the list - the order is crucial. So, in our case, if rows of the data set have the same value in the column 'Flight', they will relate to one group. Then inside those groups, the function finds other groups for rows with the same value in the column 'Airline'. Then, due to the method .count() that counts the rows, our function will calculate the number of rows in the column 'Delay' that have the same value in the column 'Airline' for each 'Flight' group.

Tarefa

Your task here is to group data by the airport from which the flight started and then by the weekday. Calculate the average time for the groups. Follow the algorithm to manage the task:

  1. Group data:
    • Extract the columns 'AirportFrom', 'DayOfWeek', and 'Time' from data (in this order);
    • Apply the .groupby() method to the previous columns;
    • Within the .groupby() method, put the columns 'AirportFrom' and 'DayOfWeek'; the order is crucial;
    • Calculate the mean value of the column 'Time'.
  2. Output the first 10 rows of the data_flights.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Let's add some information on the .groupby() method. You can group by several columns, but the order is crucial in this case. In the previous chapter, we grouped data by the flight number and counted the number of delays. We can make this task complicated by grouping not only by the 'Flight' column, but also by the column 'Airline'. Refresh the information on the dataset and then look at this simple example (the output contains only the first 10 rows):


              
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', 'Delay', 'Airline']].groupby(['Flight', 'Airline']).count()
print(data_flights.head(10))
copy

Explanation:

  • data[['Flight', 'Delay', 'Airline']] - columns you will work with, including the columns by which you will group;
  • .groupby(['Flight', 'Airline']) - here, 'Flight' and 'Airline' are arguments of the function .groupby().

Pay attention; if you want to group by several columns, put them into the list - the order is crucial. So, in our case, if rows of the data set have the same value in the column 'Flight', they will relate to one group. Then inside those groups, the function finds other groups for rows with the same value in the column 'Airline'. Then, due to the method .count() that counts the rows, our function will calculate the number of rows in the column 'Delay' that have the same value in the column 'Airline' for each 'Flight' group.

Tarefa

Your task here is to group data by the airport from which the flight started and then by the weekday. Calculate the average time for the groups. Follow the algorithm to manage the task:

  1. Group data:
    • Extract the columns 'AirportFrom', 'DayOfWeek', and 'Time' from data (in this order);
    • Apply the .groupby() method to the previous columns;
    • Within the .groupby() method, put the columns 'AirportFrom' and 'DayOfWeek'; the order is crucial;
    • Calculate the mean value of the column 'Time'.
  2. Output the first 10 rows of the data_flights.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 4. Capítulo 2
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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