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Python: Exploratory Data Analysis
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Python: Exploratory Data Analysis
50 exercises
·
3 free
01
Inspect the DataFrame
Free
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02
Print shape and columns
Free
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03
Compute Mean, Median, Mode Of Sepal_length
Free
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04
Compute Correlation
Locked
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05
Print Unique Values Of Species
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06
Count Rows With Sepal_length > 5
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07
Max Petal_width And Corresponding Row
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08
Sort By Species Ascending And Sepal_length Descending
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09
Percentage Of Missing Values
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10
Species Counts
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11
Filter Rows For A Specific Species
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12
Group By Species And Compute Mean
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13
Create A New Column 'Petal_ratio'
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14
Identify Rows With Missing Values
Locked
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15
Replace Missing Values In Sepal_length
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16
Count Unique Species
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17
Summary Statistics
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18
Sort By Sepal_length Ascending
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19
Drop Rows With Missing Values
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20
Correlation Between Sepal_length And Petal_length
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21
Select Numeric Columns Only
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22
Rename Columns
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23
Restore Original Column Names Demonstration
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24
Query Method Usage
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25
Value Counts With Normalize=True
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26
Insert A New Column 'Sepal_area'
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27
Dataframe Information
Locked
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28
Filter By Multiple Conditions
Locked
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29
Count Nulls In Each Row
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30
Group By Species And Get Standard Deviation
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31
Find Outlier Rows In Petal_length (Simple Approach)
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32
Sample Random Rows
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33
Reorder Columns
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34
Convert Species To Uppercase
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35
Convert Species Back To Title-Case
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36
Create A Cross-Tab Of Species Vs. A Binned Sepal_length
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37
Drop The Temporary 'Length_bin' Column
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38
Find Rows With Species 'Versicolor'
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39
Convert A Numeric Column To String Type
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40
Convert Sepal_width Back To Float
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41
Create A Pivot Table With Species As Rows
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42
Sort By Multiple Columns Again
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43
Count Species Per Sepal_length Bin
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44
Reset Index After Grouping
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45
Basic Stats By Species With Agg
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46
Filter And Assign Example
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47
Double-Check Data Types
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48
Identify Potential Duplicates
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49
Keep Only Numeric Columns For Correlation Matrix
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50
Rename Species Back To All-Lowercase
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