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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

bookAdvanced Grouping

Let's expand our knowledge on the .groupby() method. As you remember, we can use the .agg() method. Indeed, the main pros of this function are that we can apply a different function to the numerical columns with one group key. Look at the example where we grouped flights by the column 'Airline', then counted the values in 'Delay' for each 'Airline', and calculated the minimum and maximum values for the 'Length' column. So convenient, isn't it?


              
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.groupby('Airline').agg({'Delay': 'count', 'Length': ['min', 'max']})
print(data_flights.head(10))
copy

Explanation:

  • .agg() - a method that allows us to apply functions on a series or on each element separately;
  • {} - we use curly brackets to specify the column and apply functions to them directly;
  • 'Delay': 'count' - applies the .count() function to the values in the 'Delay' column having the same key group;
  • 'Length': ['min', 'max'] - applies the .min() and .max() functions to the values in the 'Length' column having the same key group. You just need to put just the column name without the () or . symbols in the function. Pay attention; if you want to apply several functions to the same column, you must put them into the list.

Tarea

We can assume that a delay depends on the airline or the airport, but let's dive deeper and look at the average and maximum delay times depending on the airport from which the flight started and then on the airport at which the flight ended. Also, look at the median length of the flight. Follow the algorithm:

Group data:

  • Apply the .groupby() method to the dataset data;
  • Within the .groupby() method, put the columns 'AirportFrom' and 'AirportTo'; the order is crucial;
  • Using the .agg() method, calculate the aggregated values: the average and maximum value in the column 'Time', and the median value of the column 'Length'.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 4
toggle bottom row

bookAdvanced Grouping

Let's expand our knowledge on the .groupby() method. As you remember, we can use the .agg() method. Indeed, the main pros of this function are that we can apply a different function to the numerical columns with one group key. Look at the example where we grouped flights by the column 'Airline', then counted the values in 'Delay' for each 'Airline', and calculated the minimum and maximum values for the 'Length' column. So convenient, isn't it?


              
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.groupby('Airline').agg({'Delay': 'count', 'Length': ['min', 'max']})
print(data_flights.head(10))
copy

Explanation:

  • .agg() - a method that allows us to apply functions on a series or on each element separately;
  • {} - we use curly brackets to specify the column and apply functions to them directly;
  • 'Delay': 'count' - applies the .count() function to the values in the 'Delay' column having the same key group;
  • 'Length': ['min', 'max'] - applies the .min() and .max() functions to the values in the 'Length' column having the same key group. You just need to put just the column name without the () or . symbols in the function. Pay attention; if you want to apply several functions to the same column, you must put them into the list.

Tarea

We can assume that a delay depends on the airline or the airport, but let's dive deeper and look at the average and maximum delay times depending on the airport from which the flight started and then on the airport at which the flight ended. Also, look at the median length of the flight. Follow the algorithm:

Group data:

  • Apply the .groupby() method to the dataset data;
  • Within the .groupby() method, put the columns 'AirportFrom' and 'AirportTo'; the order is crucial;
  • Using the .agg() method, calculate the aggregated values: the average and maximum value in the column 'Time', and the median value of the column 'Length'.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 4
toggle bottom row

bookAdvanced Grouping

Let's expand our knowledge on the .groupby() method. As you remember, we can use the .agg() method. Indeed, the main pros of this function are that we can apply a different function to the numerical columns with one group key. Look at the example where we grouped flights by the column 'Airline', then counted the values in 'Delay' for each 'Airline', and calculated the minimum and maximum values for the 'Length' column. So convenient, isn't it?


              
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.groupby('Airline').agg({'Delay': 'count', 'Length': ['min', 'max']})
print(data_flights.head(10))
copy

Explanation:

  • .agg() - a method that allows us to apply functions on a series or on each element separately;
  • {} - we use curly brackets to specify the column and apply functions to them directly;
  • 'Delay': 'count' - applies the .count() function to the values in the 'Delay' column having the same key group;
  • 'Length': ['min', 'max'] - applies the .min() and .max() functions to the values in the 'Length' column having the same key group. You just need to put just the column name without the () or . symbols in the function. Pay attention; if you want to apply several functions to the same column, you must put them into the list.

Tarea

We can assume that a delay depends on the airline or the airport, but let's dive deeper and look at the average and maximum delay times depending on the airport from which the flight started and then on the airport at which the flight ended. Also, look at the median length of the flight. Follow the algorithm:

Group data:

  • Apply the .groupby() method to the dataset data;
  • Within the .groupby() method, put the columns 'AirportFrom' and 'AirportTo'; the order is crucial;
  • Using the .agg() method, calculate the aggregated values: the average and maximum value in the column 'Time', and the median value of the column 'Length'.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Let's expand our knowledge on the .groupby() method. As you remember, we can use the .agg() method. Indeed, the main pros of this function are that we can apply a different function to the numerical columns with one group key. Look at the example where we grouped flights by the column 'Airline', then counted the values in 'Delay' for each 'Airline', and calculated the minimum and maximum values for the 'Length' column. So convenient, isn't it?


              
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.groupby('Airline').agg({'Delay': 'count', 'Length': ['min', 'max']})
print(data_flights.head(10))
copy

Explanation:

  • .agg() - a method that allows us to apply functions on a series or on each element separately;
  • {} - we use curly brackets to specify the column and apply functions to them directly;
  • 'Delay': 'count' - applies the .count() function to the values in the 'Delay' column having the same key group;
  • 'Length': ['min', 'max'] - applies the .min() and .max() functions to the values in the 'Length' column having the same key group. You just need to put just the column name without the () or . symbols in the function. Pay attention; if you want to apply several functions to the same column, you must put them into the list.

Tarea

We can assume that a delay depends on the airline or the airport, but let's dive deeper and look at the average and maximum delay times depending on the airport from which the flight started and then on the airport at which the flight ended. Also, look at the median length of the flight. Follow the algorithm:

Group data:

  • Apply the .groupby() method to the dataset data;
  • Within the .groupby() method, put the columns 'AirportFrom' and 'AirportTo'; the order is crucial;
  • Using the .agg() method, calculate the aggregated values: the average and maximum value in the column 'Time', and the median value of the column 'Length'.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 4. Capítulo 4
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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