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Advanced Techniques in pandas
course content

Course Content

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

bookDealing With Pivot Tables

Python has an analog of the .groupby() method that can lead to the same result. It is up to you as to which function to use. Let's learn it by using an example. Using the following function, called .pivot_table(), we will calculate the mean values of the column 'Length' that have the same value in the column 'Flight':


              
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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)

# The code using .groupby()
data_flights_1 = data[['Length', 'Flight']].groupby('Flight').mean()
# The same code using .groupby()
data_flights_2 = data[['Length', 'Flight']].groupby('Flight').agg('mean')

# The same code using .pivot_table()
data_flights_3 = pd.pivot_table(data, values = 'Length',
                      index = 'Flight',
                      aggfunc = 'mean')

print(data_flights_1.head())
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Explanation:

  • pd.pivot_table() - function that creates pivot tables;
  • data - data frame that we use;
  • values = 'Length' - to the argument values, we assign columns having the same group, for which we will apply the calculation of the average, maximum, etc. If you want to group by several columns, put them in the list; the order isn't crucial;
  • index = 'Flight' - index is an argument to which you assign the name of a column or columns that you want to group. If you want to group by several columns, put them in the list; the order is crucial, like in the .groupby() function;
  • aggfunc = 'mean' - the same as agg in the .groupby() method, aggfunc has exactly the same syntax as agg. Thus, you can put several functions here by putting them in the list to specify functions for different columns using curly brackets.
Here, you have the example of a code that uses the `.groupby()` statement. Please choose the analog using the `.pivot_table()` function.

Here, you have the example of a code that uses the .groupby() statement. Please choose the analog using the .pivot_table() function.

Select the correct answer

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Section 4. Chapter 5
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