Simple data exploration

In this notebook we will explore a dataset from an article by a team at autodesk (which I link to below). We can think of this as the simple data exploration you might do when you first start working with a new dataset.

First, we will load pandas and numpy, and read the comma-separated-value (CSV) file into a pandas dataframe.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
# figure size in inches
rcParams['figure.figsize'] = 6,6
df = pd.read_csv("./assets/DatasaurusDozen-long.csv")
x y label
0 32.331110 61.411101 away
1 53.421463 26.186880 away
2 63.920202 30.832194 away
3 70.289506 82.533649 away
4 34.118830 45.734551 away
... ... ... ...
1841 34.794594 13.969683 x_shape
1842 79.221764 22.094591 x_shape
1843 36.030880 93.121733 x_shape
1844 34.499558 86.609985 x_shape
1845 31.106867 89.461635 x_shape

1846 rows × 3 columns

From the webpage where I found the dataset, I know that really this dataframe has merged 13 sub-datasets that are labeled by the collumn label.

Take action

Let’s check to see what the unique values for the label are.

array(['away', 'bullseye', 'circle', 'dino', 'dots', 'h_lines',
       'high_lines', 'slant_down', 'slant_up', 'star', 'v_lines',
       'wide_lines', 'x_shape'], dtype=object)

Take action

Let’s check the mean and covariance for x and y for the label='dots'.

We will use the selection by callable functionality in pandas.

selector = lambda df: df['label'] =='dino'
x y
x 281.069988 -29.113933
y -29.113933 725.515961
x    54.263273
y    47.832253
dtype: float64

Ok, great, so we know the mean and the covariance for the data in the dino sub-dataset.

Visualize the data

Stop and think

What do you think the data looks like?

Maybe you think it looks like a 2-d Gaussian, that’s reasoanble since all we know is the mean and the covariance.

Take action

Let’s make a synthetic dataset with that mean and covariance.

# convert mean and cov to a numpy array:
np_mean = df.loc[lambda df: df['label'] =='dino', :].mean().to_numpy()
np_cov = df.loc[lambda df: df['label'] =='dino', :].cov().to_numpy()
# generate some synthetic data
n_to_generate = df.loc[selector,'label'].count()
norm_data = np.random.multivariate_normal(mean=np_mean, cov=np_cov,size=n_to_generate)
#df.loc[lambda df: df['label'] =='dots', :].plot('x','y',kind='scatter', c='red', alpha=.5,label='dino')
plt.scatter(norm_data[:,0],norm_data[:,1], c='blue', alpha=.5, label='synthetic')
<matplotlib.legend.Legend at 0x7fbe806a7a60>

Take action

Let’s compare the synthetic dataset with the actual data

df.loc[lambda df: df['label'] =='dino', :].plot('x','y',kind='scatter', c='red', alpha=.5,label='label == dino')
#plt.scatter(norm_data[:,0],norm_data[:,1], c='blue', alpha=.5, label='synthetic')
<matplotlib.legend.Legend at 0x7fbea28a02e0>

The synthetic data and the real data don’t look similar at all. The mean and covariance aren’t telling us the full story


Visualizing your data is a good idea, simple summary statistics don’t tell the full story.

Repeat using pandas “groupby” functionality

Take action

Now let’s check the covariance between x and y grouped by all the labels

Notice that each sub-dataset has essentially the same 2x2 covariance matrix.

x y
away x 281.227029 -28.971572
y -28.971572 725.749775
bullseye x 281.207393 -30.979902
y -30.979902 725.533372
circle x 280.898024 -30.846620
y -30.846620 725.226844
dino x 281.069988 -29.113933
y -29.113933 725.515961
dots x 281.156953 -27.247681
y -27.247681 725.235215
h_lines x 281.095333 -27.874816
y -27.874816 725.756931
high_lines x 281.122364 -30.943012
y -30.943012 725.763490
slant_down x 281.124206 -31.153399
y -31.153399 725.553749
slant_up x 281.194420 -30.992806
y -30.992806 725.688605
star x 281.197993 -28.432772
y -28.432772 725.239695
v_lines x 281.231512 -31.371608
y -31.371608 725.638809
wide_lines x 281.232887 -30.075267
y -30.075267 725.650560
x_shape x 281.231481 -29.618418
y -29.618418 725.224991

… and the means are the same too.

x y
away 54.266100 47.834721
bullseye 54.268730 47.830823
circle 54.267320 47.837717
dino 54.263273 47.832253
dots 54.260303 47.839829
h_lines 54.261442 47.830252
high_lines 54.268805 47.835450
slant_down 54.267849 47.835896
slant_up 54.265882 47.831496
star 54.267341 47.839545
v_lines 54.269927 47.836988
wide_lines 54.266916 47.831602
x_shape 54.260150 47.839717

Take action

Let’s make scatter plots grouped by the label for the sub-dataset.

away          AxesSubplot(0.125,0.125;0.775x0.755)
bullseye      AxesSubplot(0.125,0.125;0.775x0.755)
circle        AxesSubplot(0.125,0.125;0.775x0.755)
dino          AxesSubplot(0.125,0.125;0.775x0.755)
dots          AxesSubplot(0.125,0.125;0.775x0.755)
h_lines       AxesSubplot(0.125,0.125;0.775x0.755)
high_lines    AxesSubplot(0.125,0.125;0.775x0.755)
slant_down    AxesSubplot(0.125,0.125;0.775x0.755)
slant_up      AxesSubplot(0.125,0.125;0.775x0.755)
star          AxesSubplot(0.125,0.125;0.775x0.755)
v_lines       AxesSubplot(0.125,0.125;0.775x0.755)
wide_lines    AxesSubplot(0.125,0.125;0.775x0.755)
x_shape       AxesSubplot(0.125,0.125;0.775x0.755)
dtype: object
_images/datasaurus-long_19_1.png _images/datasaurus-long_19_2.png _images/datasaurus-long_19_3.png _images/datasaurus-long_19_4.png _images/datasaurus-long_19_5.png _images/datasaurus-long_19_6.png _images/datasaurus-long_19_7.png _images/datasaurus-long_19_8.png _images/datasaurus-long_19_9.png _images/datasaurus-long_19_10.png _images/datasaurus-long_19_11.png _images/datasaurus-long_19_12.png _images/datasaurus-long_19_13.png

Other types of data visualization

In addition to scatter plots, we can also use the seaborn library to make violin plots, which allows us to compare individual marginal distributions grouped by the sub-dataset label.

import seaborn as sns
ax = sns.violinplot(x="label",y="x", data=df)
#fig = plt.gcf()
#fig.set_size_inches(15, 5)
ax = sns.violinplot(x="label",y="y", data=df,figsize=(15,5))

We can also use boxplots

ax = sns.boxplot(x="label",y="y", data=df)


Final thoughts

The data set above comes from this post by Autodesk research:

 Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing
 CHI 2017 Conference proceedings:
 ACM SIGCHI Conference on Human Factors in Computing Systems

It was inspired by this tweet from Alberto Cairo:

A more well-known example is known as Anscombe Quartet

This wikipedia article on Correlation and Dependence is also great, the bottom row shows examples of two variables that are uncorrelated, but not statistically independent, eg. we can’t factorize the joint \(p(X,Y)\) as \(p(X)p(Y)\).

Correlation examples2.svg

Finally, here’s a cool tool for quickly making a csv dataset: Draw my data

And finally, here’s a short video from Autodesk: