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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

Is Data in ...?Is Data in ...?

In this section, we will continue extracting data using specific conditions. Here, you will become familiar with the helpful function called .isin(). But firstly, you need to examine the dataset. Look at the first five rows:

Now, take a look at the example and the explanation below:

Explanation:

If you remember, we always put the conditions inside the .loc[] function. Here, we do the same. The .isin(list) function checks if the values from the column are in the array. In our case, we check if values from the column 'Manufacturer' are in the list models.

Task

Your task here is to extract data about cars where values from the column 'Color' are equal to 'Grey', 'White', 'Black'. Follow the algorithm to easily manage with the task:

  1. Create the colors list with the elements 'Grey', 'White', 'Black' (in this order).
  2. Extract values from the column 'Color' that the list color consists of. Use the .loc[] function.
  3. Output the last five rows of the dataset data_extracted.

Everything was clear?

Section 3. Chapter 1
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