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Advanced Techniques in pandas
course content

Course Content

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
Outputting Columns by TitleOutputting Rows by IndexSelecting Specific Rows and ColumnsLearning More About IndexationGetting Familiar With lambda FunctionsExpanding Functionality of the .iloc[] Attribute
2. Dealing With Conditions
Setting ConditionSelecting Data Based on ConditionDealing With Several ConditionsMaking Your Code BeautifulConditions Quiz
3. Extracting Data
Is Data in ...?Combining Your KnowledgeBetween MethodExtracting Specific DataFinding the Smallest Values of a ColumnFinding the Largest Values of a ColumnFinding the Correlation
4. Aggregating Data
Getting Familiar With the .groupby() MethodGrouping by Several ColumnsComplicated GroupingAdvanced GroupingDealing With Pivot TablesCreating a Pivot Table
5. Preprocessing Data
Checking for Missing ValuesCalculating the Number of Missing ValuesWhat Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing ValuesManaging Categorical VariablesChecking the Column TypeManaging an Incorrect ColumnRenaming the Column

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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)
copy

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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Output the type of the column 'Fare'.

print(data[''].)
object

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SectionΒ 5. ChapterΒ 7
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