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Advanced Techniques in pandas

Complicated GroupingComplicated 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.

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() function.

  • .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.

Завдання

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() function; 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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Секція 4. Розділ 3
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Зміст курсу

Advanced Techniques in pandas

1. Get Familiar With Indexing and Selecting Data

Output Columns by TitleOutput Rows by IndexSelect Specific Rows and ColumnsLearn More About IndexationGet Familiar With lambda FunctionsExpand Functionality of the .iloc[] Function

2. Dealing With Conditions

Set ConditionSelect Data Based on ConditionDealing With Several ConditionsMake Your Code BeautifulConditions Quiz

3. Extract Data

Is Data in ...?Combine Your KnowledgeBetween FunctionExtract Specific DataFind the Smallest Values of a ColumnFind the Largest Values of a ColumnFind the Correlation

4. Aggregating Data

Get Familiar With the .groupby() MethodGroup by Several ColumnsComplicated GroupingGroupDealing With Pivot TablesCreate a Pivot Table

5. Preprocessing Data

Check for Missing ValuesCalculate the Number of Missing ValuesWhat Will We Do With the NaN Values?How to Delete Only NaN Values?Fill In the Missing ValuesManage Categorical VariablesCheck the Column TypeManage an Incorrect ColumnRename the Column

Advanced Techniques in pandas

Complicated GroupingComplicated 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.

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() function.

  • .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.

Завдання

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() function; 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.

Все було зрозуміло?

Секція 4. Розділ 3
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