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sidetable

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sidetable is a supercharged combination of pandas value_counts plus crosstab that builds simple but useful summary tables of your pandas DataFrame. sidetable can also add subtotals to your DataFrame.

Usage is straightforward. Install and import sidetable. Then access it through the new .stb accessor on your DataFrame.

For the Titanic data: df.stb.freq(['class']) will build a frequency table like this:

class count percent cumulative_count cumulative_percent
0 Third 491 55.1066 491 55.1066
1 First 216 24.2424 707 79.349
2 Second 184 20.651 891 100

You can also summarize missing values with df.stb.missing():

missing total percent
deck 688 891 77.2166
age 177 891 19.8653
embarked 2 891 0.224467
embark_town 2 891 0.224467
survived 0 891 0
pclass 0 891 0
sex 0 891 0
sibsp 0 891 0
parch 0 891 0
fare 0 891 0
class 0 891 0
who 0 891 0
adult_male 0 891 0
alive 0 891 0
alone 0 891 0

You can group the data and add subtotals and grand totals with stb.subtotal():

df.groupby(['sex', 'class']).agg({'fare': ['sum']}).stb.subtotal()
fare
sum
sex class
female First 9975.8250
Second 1669.7292
Third 2321.1086
female - subtotal 13966.6628
male First 8201.5875
Second 2132.1125
Third 4393.5865
male - subtotal 14727.2865
grand_total 28693.9493

sidetable has several useful features:

  • See total counts and their relative percentages in one table. This is roughly equivalent to combining the output of value_counts() and value_counts(normalize=True) into one table.
  • Include cumulative totals and percentages to better understand your thresholds. The Pareto principle applies to many different scenarios and this function makes it easy to see how your data is cumulatively distributed.
  • Aggregate multiple columns together to see frequency counts for grouped data.
  • Provide a threshold point above which all data is grouped into a single bucket. This is useful for quickly identifying the areas to focus your analysis.
  • Get a count of the missing values in your data.
  • Count the number of unique values for each column.
  • Add grand totals on any DataFrame and subtotals to any grouped DataFrame

Table of Contents:

Quickstart

For the impatient:

$ python -m pip install sidetable
import sidetable
import pandas as pd

# Create your DataFrame
df = pd.read_csv(myfile.csv)

# Build a frequency table for one or more columns
df.stb.freq(['column1', 'column2'])

# See what data is missing
df.stb.missing()

# Group data and add a subtotal
df.groupby(['column1', 'column2'])['col3'].sum().stb.subtotal()

That's it.

Read on for more details and more examples of what you can do sidetable.

Rationale

The idea behind sidetable is that there are a handful of useful data analysis tasks that you might run on any data set early in the data analysis process. While each of these tasks can be done in a handful of lines of pandas code, it is a lot of typing and difficult to remember.

In addition to providing useful functionality, this project is also a test to see how to build custom accessors using some of pandas relatively new API. I am hopeful this can serve as a model for other projects whether open source or just for your own usage. Please check out the release announcement for more information about the usage and how to use this as a model for your own projects.

The solutions in sidetable are heavily based on three sources:

  • This tweet thread by Peter Baumgartner
  • An excellent article by Steve Miller that lays out many of the code concepts incorporated into sidetable.
  • Ted Petrou's post on finding the percentage of missing values in a DataFrame.

I very much appreciate the work that all three authors did to point me in this direction.

Installation


$  python -m pip install -U sidetable

This is the preferred method to install sidetable, as it will always install the most recent stable release. sidetable requires pandas 1.0 or higher and no additional dependencies. It should run anywhere that pandas runs.

If you prefer to use conda, sidetable is available on conda-forge:

$ conda install -c conda-forge sidetable

Usage

import pandas as pd
import sidetable
import seaborn as sns

df = sns.load_dataset('titanic')

sidetable uses the pandas DataFrame accessor api to add a .stb accessor to all of your DataFrames. Once you import sidetable you are ready to go. In these examples, I will be using seaborn's Titanic dataset as an example but seaborn is not a direct dependency.

freq

If you have used value_counts() before, you have probably wished it were easier to combine the values with percentage distribution.

df['class'].value_counts()

Third     491
First     216
Second    184
Name: class, dtype: int64

df['class'].value_counts(normalize=True)

Third     0.551066
First     0.242424
Second    0.206510
Name: class, dtype: float64

Which can be done, but is messy and a lot of typing and remembering:

pd.concat([df['class'].value_counts().rename('count'), 
        df['class'].value_counts(normalize=True).mul(100).rename('percentage')], axis=1)
count percentage
Third 491 55.1066
First 216 24.2424
Second 184 20.651

Using sidetable is much simpler and you get cumulative totals, percents and more flexibility:

df.stb.freq(['class'])
class count percent cumulative_count cumulative_percent
0 Third 491 55.1066 491 55.1066
1 First 216 24.2424 707 79.349
2 Second 184 20.651 891 100

If you want to style the results so percentages and large numbers are easier to read, use style=True:

df.stb.freq(['class'], style=True)
class count percent cumulative_count cumulative_percent
0 Third 491 55.11% 491 55.11%
1 First 216 24.24% 707 79.35%
2 Second 184 20.65% 891 100.00%

In addition, you can group columns together. If we want to see the breakdown among class and sex:

df.stb.freq(['sex', 'class'])
sex class count percent cumulative_count cumulative_percent
0 male Third 347 38.945 347 38.945
1 female Third 144 16.1616 491 55.1066
2 male First 122 13.6925 613 68.7991
3 male Second 108 12.1212 721 80.9203
4 female First 94 10.5499 815 91.4703
5 female Second 76 8.52974 891 100

You can use as many groupings as you would like.

By default, sidetable counts the data. However, you can specify a value argument to indicate that the data should be summed based on the data in another column. For this data set, we can see how the fares are distributed by class:

df.stb.freq(['class'], value='fare')
class fare percent cumulative_fare cumulative_percent
0 First 18177.4 63.3493 18177.4 63.3493
1 Third 6714.7 23.4011 24892.1 86.7504
2 Second 3801.84 13.2496 28693.9 100

Another feature of sidetable is that you can specify a threshold. For many data analysis, you may want to break down into large groupings to focus on and ignore others. You can use the thresh argument to define a threshold and group all entries above that threshold into an "other" grouping:

df.stb.freq(['class', 'who'], value='fare', thresh=80)
class who fare percent cumulative_fare cumulative_percent
0 First woman 9492.94 33.0834 9492.94 33.0834
1 First man 7848.18 27.3513 17341.1 60.4348
2 Third man 3617.53 12.6073 20958.6 73.042
3 Second man 1886.36 6.57406 22845 79.6161
4 others others 5848.95 20.3839 28693.9 100

You can further customize by specifying the label to use for all the others:

df.stb.freq(['class', 'who'], value='fare', thresh=80, other_label='All others')
class who fare percent cumulative_fare cumulative_percent
0 First woman 9492.94 33.0834 9492.94 33.0834
1 First man 7848.18 27.3513 17341.1 60.4348
2 Third man 3617.53 12.6073 20958.6 73.042
3 Second man 1886.36 6.57406 22845 79.6161
4 All others All others 5848.95 20.3839 28693.9 100

counts

The counts() function shows how many unique values are in each column as well as the most and least frequent values & their total counts. This summary view can help you determine if you need to convert data to a categorical value. It can also help you understand the high level structure of your data.

df.stb.counts()
count unique most_freq most_freq_count least_freq least_freq_count
survived 891 2 0 549 1 342
sex 891 2 male 577 female 314
adult_male 891 2 True 537 False 354
alive 891 2 no 549 yes 342
alone 891 2 True 537 False 354
pclass 891 3 3 491 2 184
embarked 889 3 S 644 Q 77
class 891 3 Third 491 Second 184
who 891 3 man 537 child 83
embark_town 889 3 Southampton 644 Queenstown 77
sibsp 891 7 0 608 5 5
parch 891 7 0 678 6 1
deck 203 7 C 59 G 4
age 714 88 24.0 30 20.5 1
fare 891 248 8.05 43 63.3583 1

By default, all data types are included but you may use the exclude and include parameters to select specific types of columns. The syntax is the same as pandas select_dtypes

For example,

df.stb.counts(exclude='number')
count unique most_freq most_freq_count least_freq least_freq_count
sex 891 2 male 577 female 314
adult_male 891 2 True 537 False 354
alive 891 2 no 549 yes 342
alone 891 2 True 537 False 354
embarked 889 3 S 644 Q 77
class 891 3 Third 491 Second 184
who 891 3 man 537 child 83
embark_town 889 3 Southampton 644 Queenstown 77
deck 203 7 C 59 G 4

missing

sidetable also includes a summary table that shows the missing values in your data by count and percentage of total missing values in a column.

df.stb.missing()
missing total percent
deck 688 891 77.2166
age 177 891 19.8653
embarked 2 891 0.224467
embark_town 2 891 0.224467
survived 0 891 0
pclass 0 891 0
sex 0 891 0
sibsp 0 891 0
parch 0 891 0
fare 0 891 0
class 0 891 0
who 0 891 0
adult_male 0 891 0
alive 0 891 0
alone 0 891 0

If you wish to see the results with styles applied to the Percent and Total column, use:

df.stb.missing(style=True)
missing total percent
deck 688 891 77.22%
age 177 891 19.87%
embarked 2 891 0.22%
embark_town 2 891 0.22%
survived 0 891 0
pclass 0 891 0
sex 0 891 0
sibsp 0 891 0
parch 0 891 0
fare 0 891 0
class 0 891 0
who 0 891 0
adult_male 0 891 0
alive 0 891 0
alone 0 891 0

Finally, you can exclude the columns that have 0 missing values using the clip_0=True parameter:

df.stb.missing(clip_0=True, style=True)
missing total percent
deck 688 891 77.22%
age 177 891 19.87%
embarked 2 891 0.22%
embark_town 2 891 0.22%

subtotal

Another useful function is the subtotal function. Trying to add a subtotal to grouped pandas data is not easy. sidetable adds a subtotal() function that makes adds a subtotal at one or more levels of a DataFrame.

The subtotal function can be applied to a simple DataFrame in order to add a Grand Total label:

df.stb.subtotal()
survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
887 1 1 female 19 0 0 30 S First woman 0 B Southampton yes 1
888 0 3 female nan 1 2 23.45 S Third woman 0 nan Southampton no 0
889 1 1 male 26 0 0 30 C First man 1 C Cherbourg yes 1
890 0 3 male 32 0 0 7.75 Q Third man 1 nan Queenstown no 1
grand_total 342 2057 nan 21205.2 466 340 28693.9 nan nan nan 537 nan nan nan 537

The real power of subtotal is being able to add it to one or more levels of your grouped data. For example, you can group the data and add a subtotal at each level:

df.groupby(['sex', 'class', 'embark_town']).agg({'fare': ['sum']}).stb.subtotal()

Which yields this view (truncated for simplicity):

fare
sum
sex class embark_town
female First Cherbourg 4972.5333
Queenstown 90.0000
Southampton 4753.2917
female | First - subtotal 9815.8250
Second Cherbourg 176.8792
Queenstown 24.7000
Southampton 1468.1500
female | Second - subtotal 1669.7292
Third Cherbourg 337.9833
Queenstown 340.1585
Southampton 1642.9668
female | Third - subtotal 2321.1086
female - subtotal 13806.6628
male First Cherbourg 3928.5417
Queenstown 90.0000

By default, every level in the DataFrame will be subtotaled but you can control this behavior by using the sub_level argument. For instance, you can subtotal on sex and class by passing the argument sub_level=[1,2]

summary_table = df.groupby(['sex', 'class', 'embark_town']).agg({'fare': ['sum']})
summary_table.stb.subtotal(sub_level=[1, 2])

The subtotal function also allows the user to configure the labels and separators used in the subtotal and Grand Total by using the grand_label, sub_label, show_sep and sep arguments.

Caveats

sidetable supports grouping on any data type in a pandas DataFrame. This means that you could try something like:

df.stb.freq(['fare'])

In some cases where there are a fairly small discrete number of this may be useful. However, if you have a lot of unique values, you should bin the data first. In the example, above the data would include 248 rows and not be terribly useful.

One alternative could be:

df['fare_bin'] = pd.qcut(df['fare'], q=4, labels=['low', 'medium', 'high', 'x-high'])
df.stb.freq(['fare_bin'])
fare_bin count percent cumulative_count cumulative_percent
0 medium 224 25.1403 224 25.1403
1 low 223 25.0281 447 50.1684
2 x-high 222 24.9158 669 75.0842
3 high 222 24.9158 891 100

The other caveat is that null or missing values can cause data to drop out while aggregating. For instance, if we look at the deck variable, there are a lot of missing values.

df.stb.freq(['deck'])
deck count percent cumulative_count cumulative_percent
0 C 59 29.064 59 29.064
1 B 47 23.1527 106 52.2167
2 D 33 16.2562 139 68.4729
3 E 32 15.7635 171 84.2365
4 A 15 7.38916 186 91.6256
5 F 13 6.40394 199 98.0296
6 G 4 1.97044 203 100

The total cumulative count only goes up to 203 not the 891 we have seen in other examples. Future versions of sidetable may handle this differently. For now, it is up to you to decide how best to handle unknowns. For example, this version of the Titanic data set has a categorical value for deck so using fillna requires an extra step:

df['deck_fillna'] = df['deck'].cat.add_categories('UNK').fillna('UNK')
df.stb.freq(['deck_fillna'])
deck_fillna count percent cumulative_count cumulative_percent
0 UNK 688 77.2166 688 77.2166
1 C 59 6.62177 747 83.8384
2 B 47 5.27497 794 89.1134
3 D 33 3.7037 827 92.8171
4 E 32 3.59147 859 96.4085
5 A 15 1.6835 874 98.092
6 F 13 1.45903 887 99.5511
7 G 4 0.448934 891 100

Another variant is that there might be certain groupings where there are no valid counts.

For instance, if we look at the deck and class:

df.stb.freq(['deck', 'class'])
deck class count percent cumulative_count cumulative_percent
0 C First 59 29.064 59 29.064
1 B First 47 23.1527 106 52.2167
2 D First 29 14.2857 135 66.5025
3 E First 25 12.3153 160 78.8177
4 A First 15 7.38916 175 86.2069
5 F Second 8 3.94089 183 90.1478
6 F Third 5 2.46305 188 92.6108
7 G Third 4 1.97044 192 94.5813
8 E Second 4 1.97044 196 96.5517
9 D Second 4 1.97044 200 98.5222
10 E Third 3 1.47783 203 100

There are only 11 combinations. If we want to see all - even if there are not any passengers fitting that criteria, use clip_0=False

df.stb.freq(['deck', 'class'], clip_0=False)
deck class count percent cumulative_count cumulative_percent
0 C First 59 29.064 59 29.064
1 B First 47 23.1527 106 52.2167
2 D First 29 14.2857 135 66.5025
3 E First 25 12.3153 160 78.8177
4 A First 15 7.38916 175 86.2069
5 F Second 8 3.94089 183 90.1478
6 F Third 5 2.46305 188 92.6108
7 G Third 4 1.97044 192 94.5813
8 E Second 4 1.97044 196 96.5517
9 D Second 4 1.97044 200 98.5222
10 E Third 3 1.47783 203 100
11 G Second 0 0 203 100
12 G First 0 0 203 100
13 F First 0 0 203 100
14 D Third 0 0 203 100
15 C Third 0 0 203 100
16 C Second 0 0 203 100
17 B Third 0 0 203 100
18 B Second 0 0 203 100
19 A Third 0 0 203 100
20 A Second 0 0 203 100

In many cases this might be too much data, but sometimes the fact that a combination is missing could be insightful.

The final caveat relates to subtotal. When working with the subtotal function, sidetable convert a Categorical MultiIndex to a plain index in order to easily add the subtotal labels.

TODO

  • [ ] Handle NaN values more effectively
  • [ ] Offer binning options for continuous variables
  • [ ] Offer more options, maybe plotting?

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. If you have a new idea for a simple table that we should add, please submit a ticket.

For more info please click here

Credits

This package was created with Cookiecutter and the oldani/cookiecutter-simple-pypackage project template. The code used in this package is heavily based on the posts from Peter Baumgartner, Steve Miller and Ted Petrou. Thank you!


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