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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

Challenge 1Challenge 1

To excel in programming, you always need to practice. So, in this task, you must also memorize functions from the previous chapter. If you aren't solid on the functions from the last chapter, look at the hints to revise them.

Task

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] function.
  2. Filter data; extract columns where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Everything was clear?

Section 2. Chapter 2
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