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Course Content

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 ConditionChallenge 1Dealing With Several ConditionsMake Your Code BeautifulChallenge 2

3. Extract Data

Is Data in ...?Combine Your KnowledgeBetween FunctionChallengeFind 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 TablesChallenge

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

ChallengeChallenge

Task

Your task here is to practice. You need to extract specific data on cars. Follow the algorithm:

  1. Using the.between() function create the condition to extract data on cars where 'Price' is between 15 000(inclusive) and 20 000(exclusive).
  2. Using the .between() function create the condition to extract data on cars where 'Year' is between 2015(exclusive) and 2020(exclusive).
  3. Using the.isin() function create the condition to extract data on such values from 'Fuel_type' column: 'Plug-in Hybrid' and 'Hybrid' (the values are stored in the variable fuel_types).
  4. Unite three conditions using two and statements.

Everything was clear?

Section 3. Chapter 4
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