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Advanced Techniques in pandas

Find the CorrelationFind the Correlation

Finally, let's move to the last function of this section called .corr(). It helps out a lot to find the relationship between numerical data. Imagine that you have a dataset on houses:

Price USD Number of Rooms Distance from the City Center in km
329000 4 25
8739000 6 3
1268000 6 2
987000 4 10
103000 2 30

Let's examine the output of the data.corr() in our case:

Price USD Number of Rooms Distance from the City Center in km
Price USD 1.000000 0.625651 -0.589396
Number of Rooms 0.625651 1.000000 -0.908600
Distance from the City Center in km -0.589396 -0.908600 1.000000

So, let's do it step by step: You have vertical and horizontal values; each pair overlaps. In each overlap, we can receive a value from -1 to 1.

  • 1 means that two values depend on each other in a directly proportional way (if one value increases, the other increases too);
  • -1 means that two values depend on each other in an inversely proportional way (if one value increases, the other decreases);
  • 0 means that the two dependent values aren't proportional.

Tarefa

You'll end this section with an effortless task: apply the .corr() function to the dataset. Then, try to analyze the numbers you get.

Tudo estava claro?

Seção 3. Capítulo 7
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Conteúdo do Curso

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

Find the CorrelationFind the Correlation

Finally, let's move to the last function of this section called .corr(). It helps out a lot to find the relationship between numerical data. Imagine that you have a dataset on houses:

Price USD Number of Rooms Distance from the City Center in km
329000 4 25
8739000 6 3
1268000 6 2
987000 4 10
103000 2 30

Let's examine the output of the data.corr() in our case:

Price USD Number of Rooms Distance from the City Center in km
Price USD 1.000000 0.625651 -0.589396
Number of Rooms 0.625651 1.000000 -0.908600
Distance from the City Center in km -0.589396 -0.908600 1.000000

So, let's do it step by step: You have vertical and horizontal values; each pair overlaps. In each overlap, we can receive a value from -1 to 1.

  • 1 means that two values depend on each other in a directly proportional way (if one value increases, the other increases too);
  • -1 means that two values depend on each other in an inversely proportional way (if one value increases, the other decreases);
  • 0 means that the two dependent values aren't proportional.

Tarefa

You'll end this section with an effortless task: apply the .corr() function to the dataset. Then, try to analyze the numbers you get.

Tudo estava claro?

Seção 3. Capítulo 7
toggle bottom row
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