Checking the Column Type

If you can come across the column 'Fare', the numbers here are separated with the - sign. It looks weird, doesn't it? We used to use . as the separator, and Python can understand numbers separated only with dots. Let's check the type of this column. You can do so using the attribute .dtypes. Look at the example with the column 'Age'.


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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/titanic3.csv', index_col = 0)
print(data['Age'].dtypes)

Explanation:

The .dtypes syntax is simple; you just apply it to the column or to the whole data set. In our case, the type is float64.

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Section 5. Chapter 7

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Course Content

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

2. Dealing With Conditions

Setting Condition Selecting Data Based on Condition Dealing With Several Conditions Making Your Code Beautiful Conditions Quiz

3. Extracting Data

Is Data in ...?Combining Your Knowledge Between Method Extracting Specific Data Finding the Smallest Values of a Column Finding the Largest Values of a Column Finding the Correlation

4. Aggregating Data

Getting Familiar With the .groupby() Method Grouping by Several Columns Complicated Grouping Advanced Grouping Dealing With Pivot Tables Creating a Pivot Table