Visualize your Pandas Data Transformation using PandasTutor
Visualize your Pandas Data Transformation using PandasTutor

Visualize your Pandas Data Transformation using PandasTutor

Subtitle
Visualize you Python Pandas code in your browser and see how your data transforms step-by-step
Reading Time
4 min read
Tags
PythonData ScienceVisualizationPandas
Last Updated
December 8, 2021
👉

This article is also published on Towards Data Science blog.

Table of Contents

Pandas is a powerful Python library for any exploratory data analysis. Sometimes, you may have difficulties in visualizing data transformations. Here comes PandasTutor— a web app that allows you to see how your pandas code transforms the data step-by-step.

This may come handy particularly if you have complicated transformations and want to visualize your steps or explain it to others.

PandasTutor lets you visualize different pandas transformation, from sorting to grouping by multiple columns, and even grouping by a column and performing multiple aggregations.

PandasTutor Creators

Pandas Tutor was created by Sam Lau and Philip Guo at UC San Diego. This tool is mainly developed for teaching purposes as its creator stated here. This explains some of the limitations this tool have (I will cover some of those limitations later in the post).

A similar tool called Tidy Data Tutor but for R users is created by Sean Kross and Philip Guo.

Case Study

In this article, I will provide an example where I will do a sort + group by multiple columns + performing different aggregations on multiple columns!

Dataset

Let's use the Heart Failure Prediction Dataset Kaggle Dataset (available here). You can find the data below. The data is available under Open Database (ODbl) License allowing “users to freely share, modify, and use this Database while maintaining this same freedom for others.”

Since Pandas Tutor only works with small data, I will take the first 50 rows of hearts data).

Code

Below is the code used for the visualization in this post. You may notice that the CSV data is encoded here which is a current limitation of this tool.

import pandas as pd
import io

csv = '''
Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,HeartDisease
40,M,ATA,140,289,0,Normal,172,N,0,Up,0
49,F,NAP,160,180,0,Normal,156,N,1,Flat,1
37,M,ATA,130,283,0,ST,98,N,0,Up,0
48,F,ASY,138,214,0,Normal,108,Y,1.5,Flat,1
54,M,NAP,150,195,0,Normal,122,N,0,Up,0
39,M,NAP,120,339,0,Normal,170,N,0,Up,0
45,F,ATA,130,237,0,Normal,170,N,0,Up,0
54,M,ATA,110,208,0,Normal,142,N,0,Up,0
37,M,ASY,140,207,0,Normal,130,Y,1.5,Flat,1
48,F,ATA,120,284,0,Normal,120,N,0,Up,0
37,F,NAP,130,211,0,Normal,142,N,0,Up,0
58,M,ATA,136,164,0,ST,99,Y,2,Flat,1
39,M,ATA,120,204,0,Normal,145,N,0,Up,0
49,M,ASY,140,234,0,Normal,140,Y,1,Flat,1
42,F,NAP,115,211,0,ST,137,N,0,Up,0
54,F,ATA,120,273,0,Normal,150,N,1.5,Flat,0
38,M,ASY,110,196,0,Normal,166,N,0,Flat,1
43,F,ATA,120,201,0,Normal,165,N,0,Up,0
60,M,ASY,100,248,0,Normal,125,N,1,Flat,1
36,M,ATA,120,267,0,Normal,160,N,3,Flat,1
43,F,TA,100,223,0,Normal,142,N,0,Up,0
44,M,ATA,120,184,0,Normal,142,N,1,Flat,0
49,F,ATA,124,201,0,Normal,164,N,0,Up,0
44,M,ATA,150,288,0,Normal,150,Y,3,Flat,1
40,M,NAP,130,215,0,Normal,138,N,0,Up,0
36,M,NAP,130,209,0,Normal,178,N,0,Up,0
53,M,ASY,124,260,0,ST,112,Y,3,Flat,0
52,M,ATA,120,284,0,Normal,118,N,0,Up,0
53,F,ATA,113,468,0,Normal,127,N,0,Up,0
51,M,ATA,125,188,0,Normal,145,N,0,Up,0
53,M,NAP,145,518,0,Normal,130,N,0,Flat,1
56,M,NAP,130,167,0,Normal,114,N,0,Up,0
54,M,ASY,125,224,0,Normal,122,N,2,Flat,1
41,M,ASY,130,172,0,ST,130,N,2,Flat,1
43,F,ATA,150,186,0,Normal,154,N,0,Up,0
32,M,ATA,125,254,0,Normal,155,N,0,Up,0
65,M,ASY,140,306,1,Normal,87,Y,1.5,Flat,1
41,F,ATA,110,250,0,ST,142,N,0,Up,0
48,F,ATA,120,177,1,ST,148,N,0,Up,0
48,F,ASY,150,227,0,Normal,130,Y,1,Flat,0
54,F,ATA,150,230,0,Normal,130,N,0,Up,0
54,F,NAP,130,294,0,ST,100,Y,0,Flat,1
35,M,ATA,150,264,0,Normal,168,N,0,Up,0
52,M,NAP,140,259,0,ST,170,N,0,Up,0
43,M,ASY,120,175,0,Normal,120,Y,1,Flat,1
59,M,NAP,130,318,0,Normal,120,Y,1,Flat,0
37,M,ASY,120,223,0,Normal,168,N,0,Up,0
50,M,ATA,140,216,0,Normal,170,N,0,Up,0
36,M,NAP,112,340,0,Normal,184,N,1,Flat,0
41,M,ASY,110,289,0,Normal,170,N,0,Flat,1
'''

df_hearts = pd.read_csv(io.StringIO(csv))
df_hearts = df_hearts[
    ["Age", "Sex", "RestingBP", "ChestPainType", "Cholesterol", "HeartDisease"]
]

(df_hearts.sort_values("Age")
.groupby(["Sex", "HeartDisease"])
.agg({"RestingBP": ["mean", "std"], 
      "Cholesterol": ["mean", "std"],
      "Sex": ["count"]
      })
)

So our transformations is only the last few lines

(df_hearts.sort_values("Age")
.groupby(["Sex", "HeartDisease"])
.agg({"RestingBP": ["mean", "std"], 
      "Cholesterol": ["mean", "std"],
      "Sex": ["count"]
      })
)

Results

Step 1: Sorting the DataFrame

Visualization of the
Visualization of the sort_values() result (steps 1) (generated using PandasTutor)

Step 2: Visualize Pandas Groupby operation

After sorting the results in Step 1 and visualizing it, we can visualize the groupby() operation

Visualization of the
Visualization of the groupby() result (steps 1 and 2) (generated using PandasTutor)

Step 3: Calculate different aggregations on multiple columns

Here, I will be calculating the mean and standard deviation of two columns "RestingBP" and "Cholesterol" and also provide a count for each group (here I'm using the "Sex" column to get that information.)

Visualization of the final result that is the aggregation (steps 1 - 3) (generated using
Visualization of the final result that is the aggregation (steps 1 - 3) (generated using PandasTutor)

Interesting sharing feature

Pandas Tutor also provides you with a shareable URL that even includes the CSV data used in the transformation. For instance, you can check my transformation code and results here or via below link!

https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0A%0Acsv%20%3D%20'''%0AAge,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,HeartDisease%0A40,M,ATA,140,289,0,Normal,172,N,0,Up,0%0A49,F,NAP,160,180,0,Normal,156,N,1,Flat,1%0A37,M,ATA,130,283,0,ST,98,N,0,Up,0%0A48,F,ASY,138,214,0,Normal,108,Y,1.5,Flat,1%0A54,M,NAP,150,195,0,Normal,122,N,0,Up,0%0A39,M,NAP,120,339,0,Normal,170,N,0,Up,0%0A45,F,ATA,130,237,0,Normal,170,N,0,Up,0%0A54,M,ATA,110,208,0,Normal,142,N,0,Up,0%0A37,M,ASY,140,207,0,Normal,130,Y,1.5,Flat,1%0A48,F,ATA,120,284,0,Normal,120,N,0,Up,0%0A37,F,NAP,130,211,0,Normal,142,N,0,Up,0%0A58,M,ATA,136,164,0,ST,99,Y,2,Flat,1%0A39,M,ATA,120,204,0,Normal,145,N,0,Up,0%0A49,M,ASY,140,234,0,Normal,140,Y,1,Flat,1%0A42,F,NAP,115,211,0,ST,137,N,0,Up,0%0A54,F,ATA,120,273,0,Normal,150,N,1.5,Flat,0%0A38,M,ASY,110,196,0,Normal,166,N,0,Flat,1%0A43,F,ATA,120,201,0,Normal,165,N,0,Up,0%0A60,M,ASY,100,248,0,Normal,125,N,1,Flat,1%0A36,M,ATA,120,267,0,Normal,160,N,3,Flat,1%0A43,F,TA,100,223,0,Normal,142,N,0,Up,0%0A44,M,ATA,120,184,0,Normal,142,N,1,Flat,0%0A49,F,ATA,124,201,0,Normal,164,N,0,Up,0%0A44,M,ATA,150,288,0,Normal,150,Y,3,Flat,1%0A40,M,NAP,130,215,0,Normal,138,N,0,Up,0%0A36,M,NAP,130,209,0,Normal,178,N,0,Up,0%0A53,M,ASY,124,260,0,ST,112,Y,3,Flat,0%0A52,M,ATA,120,284,0,Normal,118,N,0,Up,0%0A53,F,ATA,113,468,0,Normal,127,N,0,Up,0%0A51,M,ATA,125,188,0,Normal,145,N,0,Up,0%0A53,M,NAP,145,518,0,Normal,130,N,0,Flat,1%0A56,M,NAP,130,167,0,Normal,114,N,0,Up,0%0A54,M,ASY,125,224,0,Normal,122,N,2,Flat,1%0A41,M,ASY,130,172,0,ST,130,N,2,Flat,1%0A43,F,ATA,150,186,0,Normal,154,N,0,Up,0%0A32,M,ATA,125,254,0,Normal,155,N,0,Up,0%0A65,M,ASY,140,306,1,Normal,87,Y,1.5,Flat,1%0A41,F,ATA,110,250,0,ST,142,N,0,Up,0%0A48,F,ATA,120,177,1,ST,148,N,0,Up,0%0A48,F,ASY,150,227,0,Normal,130,Y,1,Flat,0%0A54,F,ATA,150,230,0,Normal,130,N,0,Up,0%0A54,F,NAP,130,294,0,ST,100,Y,0,Flat,1%0A35,M,ATA,150,264,0,Normal,168,N,0,Up,0%0A52,M,NAP,140,259,0,ST,170,N,0,Up,0%0A43,M,ASY,120,175,0,Normal,120,Y,1,Flat,1%0A59,M,NAP,130,318,0,Normal,120,Y,1,Flat,0%0A37,M,ASY,120,223,0,Normal,168,N,0,Up,0%0A50,M,ATA,140,216,0,Normal,170,N,0,Up,0%0A36,M,NAP,112,340,0,Normal,184,N,1,Flat,0%0A41,M,ASY,110,289,0,Normal,170,N,0,Flat,1%0A'''%0A%0Adf_hearts%20%3D%20pd.read_csv%28io.StringIO%28csv%29%29%0Adf_hearts%20%3D%20df_hearts%5B%0A%20%20%20%20%5B%22Age%22,%20%22Sex%22,%20%22RestingBP%22,%20%22ChestPainType%22,%20%22Cholesterol%22,%20%22HeartDisease%22%5D%0A%5D%0A%0A%28df_hearts.sort_values%28%22Age%22%29%0A.groupby%28%5B%22Sex%22,%20%22HeartDisease%22%5D%29%0A.agg%28%7B%22RestingBP%22%3A%20%5B%22mean%22,%20%22std%22%5D,%20%0A%20%20%20%20%20%20%22Cholesterol%22%3A%20%5B%22mean%22,%20%22std%22%5D,%0A%20%20%20%20%20%20%22Sex%22%3A%20%5B%22count%22%5D%0A%20%20%20%20%20%20%7D%29%0A%29&d=2021-12-08&lang=py&v=v1

Pros:

  • Step-by-step visualization
  • Interactive plots (you can track the data rows before and after the transformation)
  • Shareable URL

Cons (current limitations):

  • Only works for small codes (The code should be 5000bytes). Since the data is also encoded and not read from a file, hence, you can only visualize small datasets.
  • As stated in the previous step, you have to encode the data along with the code as reading from external resources (files or links) are not supported.
  • Limited Pandas' methods support.
  • You can visualize the Pandas expression only on the last line. You may have to pipe multiple steps together or run the visualizations separately.

For a complete list of unsupported features or other FAQ, you can check here.

Conclusion

In this post, we checked a nice tool for a step-by-step visualization of Pandas data transformation that generates interactive plots to compare the data before and after each transformation. This is very useful for those who want to solidify their understanding of Pandas transformation or those who wants to share those transformations with others (Pandas Tutor even provides a shareable URL).

References

Thanks for reading 🙏

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References

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