Tidy Data in Python

I recently came across a paper named Tidy Data by Hadley Wickham. Published back in 2014, the paper focuses on one aspect of cleaning up data, tidying data: structuring datasets to facilitate analysis. Through the paper, Wickham demonstrates how any dataset can be structured in a standardized way prior to analysis. He presents in detail the different types of data sets and how to wrangle them into a standard format.

As a data scientist, I think you should get very familiar with this standardized structure of a dataset. Data cleaning is one the most frequent task in data science. No matter what kind of data you are dealing with or what kind of analysis you are performing, you will have to clean the data at some point. Tidying your data in a standard format makes things easier down the road. You can reuse a standard set of tools across your different analysis.

In this post, I will summarize some tidying examples Wickham uses in his paper and I will demonstrate how to do so using the Python pandas library.

Defining tidy data

The structure Wickham defines as tidy has the following attributes:

Each variable forms a column and contains values

Each observation forms a row

Each type of observational unit forms a table

A few definitions:

Variable: A measurement or an attribute. Height, weight, sex, etc.

Value: The actual measurement or attribute. 152 cm, 80 kg, female, etc.

Observation: All values measure on the same unit. Each person.

An example of a messy dataset:

Treatment A Treatment B John Smith - 2 Jane Doe 16 11 Mary Johnson 3 1

An example of a tidy dataset:

Name Treatment Result John Smith a - Jane Doe a 16 Mary Johnson a 3 John Smith b 2 Jane Doe b 11 Mary Johnson b 1

Tidying messy datasets

Through the following examples extracted from Wickham’s paper, we’ll wrangle messy datasets into the tidy format. The goal here is not to analyze the datasets but rather prepare them in a standardized way prior to the analysis. These are the five types of messy datasets we’ll tackle:

Column headers are values, not variable names.

Multiple variables are stored in one column.

Variables are stored in both rows and columns.

Multiple types of observational units are stored in the same table.

A single observational unit is stored in multiple tables.

Note: All of the code presented in this post is available on Github.

Column headers are values, not variable names

Pew Research Center Dataset

This dataset explores the relationship between income and religion.

Problem: The columns headers are composed of the possible income values.

import pandas as pd import datetime from os import listdir from os.path import isfile , join import glob import re df = pd . read_csv ( "./data/pew-raw.csv" ) df

religion <$10k $10-20k $20-30k $30-40k $40-50k $50-75k Agnostic 27 34 60 81 76 137 Atheist 12 27 37 52 35 70 Buddhist 27 21 30 34 33 58 Catholic 418 617 732 670 638 1116 Dont know/refused 15 14 15 11 10 35 Evangelical Prot 575 869 1064 982 881 1486 Hindu 1 9 7 9 11 34 Historically Black Prot 228 244 236 238 197 223 Jehovahs Witness 20 27 24 24 21 30 Jewish 19 19 25 25 30 95

A tidy version of this dataset is one in which the income values would not be columns headers but rather values in an income column. In order to tidy this dataset, we need to melt it. The pandas library has a built-in function that allows to do just that. It “unpivots” a DataFrame from a wide format to a long format. We’ll reuse this function a few times through the post.

formatted_df = pd . melt ( df , [ "religion" ], var_name = "income" , value_name = "freq" ) formatted_df = formatted_df . sort_values ( by = [ "religion" ]) formatted_df . head ( 10 )

This outputs a tidy version of the dataset:

religion income freq Agnostic <$10k 27 Agnostic $30-40k 81 Agnostic $40-50k 76 Agnostic $50-75k 137 Agnostic $10-20k 34 Agnostic $20-30k 60 Atheist $40-50k 35 Atheist $20-30k 37 Atheist $10-20k 27 Atheist $30-40k 52

Billboard Top 100 Dataset

This dataset represents the weekly rank of songs from the moment they enter the Billboard Top 100 to the subsequent 75 weeks.

Problems:

The columns headers are composed of values: the week number ( x1st.week , …)

, …) If a song is in the Top 100 for less than 75 weeks, the remaining columns are filled with missing values.

df = pd . read_csv ( "./data/billboard.csv" , encoding = "mac_latin2" ) df . head ( 10 )

year artist.inverted track time genre date.entered date.peaked x1st.week x2nd.week ... 2000 Destiny's Child Independent Women Part I 3:38 Rock 2000-09-23 2000-11-18 78 63.0 ... 2000 Santana Maria, Maria 4:18 Rock 2000-02-12 2000-04-08 15 8.0 ... 2000 Savage Garden I Knew I Loved You 4:07 Rock 1999-10-23 2000-01-29 71 48.0 ... 2000 Madonna Music 3:45 Rock 2000-08-12 2000-09-16 41 23.0 ... 2000 Aguilera, Christina Come On Over Baby (All I Want Is You) 3:38 Rock 2000-08-05 2000-10-14 57 47.0 ... 2000 Janet Doesn't Really Matter 4:17 Rock 2000-06-17 2000-08-26 59 52.0 ... 2000 Destiny's Child Say My Name 4:31 Rock 1999-12-25 2000-03-18 83 83.0 ... 2000 Iglesias, Enrique Be With You 3:36 Latin 2000-04-01 2000-06-24 63 45.0 ... 2000 Sisqo Incomplete 3:52 Rock 2000-06-24 2000-08-12 77 66.0 ... 2000 Lonestar Amazed 4:25 Country 1999-06-05 2000-03-04 81 54.0 ...

A tidy version of this dataset is one without the week’s numbers as columns but rather as values of a single column. In order to do so, we’ll melt the weeks columns into a single date column. We will create one row per week for each record. If there is no data for the given week, we will not create a row.

# Melting id_vars = [ "year" , "artist.inverted" , "track" , "time" , "genre" , "date.entered" , "date.peaked" ] df = pd . melt ( frame = df , id_vars = id_vars , var_name = "week" , value_name = "rank" ) # Formatting df [ "week" ] = df [ 'week' ] . str . extract ( '( \ d+)' , expand = False ) . astype ( int ) df [ "rank" ] = df [ "rank" ] . astype ( int ) # Cleaning out unnecessary rows df = df . dropna () # Create "date" columns df [ 'date' ] = pd . to_datetime ( df [ 'date.entered' ]) + pd . to_timedelta ( df [ 'week' ], unit = 'w' ) - pd . DateOffset ( weeks = 1 ) df = df [[ "year" , "artist.inverted" , "track" , "time" , "genre" , "week" , "rank" , "date" ]] df = df . sort_values ( ascending = True , by = [ "year" , "artist.inverted" , "track" , "week" , "rank" ]) # Assigning the tidy dataset to a variable for future usage billboard = df df . head ( 10 )

A tidier version of the dataset is shown below. There is still a lot of repetition of the song details: the track name, time and genre. For this reason, this dataset is still not completely tidy as per Wickham’s definition. We will address this in the next example.

year artist.inverted track time genre week rank date 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 1 87 2000-02-26 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 2 82 2000-03-04 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 3 72 2000-03-11 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 4 77 2000-03-18 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 5 87 2000-03-25 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 6 94 2000-04-01 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 7 99 2000-04-08 2000 2Ge+her The Hardest Part Of Breaking Up (Is Getting Ba... 3:15 R&B 1 91 2000-09-02 2000 2Ge+her The Hardest Part Of Breaking Up (Is Getting Ba... 3:15 R&B 2 87 2000-09-09 2000 2Ge+her The Hardest Part Of Breaking Up (Is Getting Ba... 3:15 R&B 3 92 2000-09-16

Multiple types in one table

Following up on the Billboard dataset, we’ll now address the repetition problem of the previous table.

Problems:

Multiple observational units (the song and its rank ) in a single table.

We’ll first create a songs table which contains the details of each song:

songs_cols = [ "year" , "artist.inverted" , "track" , "time" , "genre" ] songs = billboard [ songs_cols ] . drop_duplicates () songs = songs . reset_index ( drop = True ) songs [ "song_id" ] = songs . index songs . head ( 10 )

year artist.inverted track time genre song_id 2000 2 Pac Baby Don't Cry (Keep Ya Head Up II) 4:22 Rap 0 2000 2Ge+her The Hardest Part Of Breaking Up (Is Getting Ba... 3:15 R&B 1 2000 3 Doors Down Kryptonite 3:53 Rock 2 2000 3 Doors Down Loser 4:24 Rock 3 2000 504 Boyz Wobble Wobble 3:35 Rap 4 2000 98� Give Me Just One Night (Una Noche) 3:24 Rock 5 2000 A*Teens Dancing Queen 3:44 Pop 6 2000 Aaliyah I Don't Wanna 4:15 Rock 7 2000 Aaliyah Try Again 4:03 Rock 8 2000 Adams, Yolanda Open My Heart 5:30 Gospel 9

We’ll then create a ranks table which only contains the song_id , date and the rank .

ranks = pd . merge ( billboard , songs , on = [ "year" , "artist.inverted" , "track" , "time" , "genre" ]) ranks = ranks [[ "song_id" , "date" , "rank" ]] ranks . head ( 10 )

song_id date rank 0 2000-02-26 87 0 2000-03-04 82 0 2000-03-11 72 0 2000-03-18 77 0 2000-03-25 87 0 2000-04-01 94 0 2000-04-08 99 1 2000-09-02 91 1 2000-09-09 87 1 2000-09-16 92

Multiple variables stored in one column

Tubercolosis Records from World Health Organization

This dataset documents the count of confirmed tuberculosis cases by country, year, age and sex.

Problems:

Some columns contain multiple values: sex and age.

Mixture of zeros and missing values NaN . This is due to the data collection process and the distinction is important for this dataset.

df = pd . read_csv ( "./data/tb-raw.csv" ) df

country year m014 m1524 m2534 m3544 m4554 m5564 m65 mu f014 AD 2000 0 0 1 0 0 0 0 NaN NaN AE 2000 2 4 4 6 5 12 10 NaN 3 AF 2000 52 228 183 149 129 94 80 NaN 93 AG 2000 0 0 0 0 0 0 1 NaN 1 AL 2000 2 19 21 14 24 19 16 NaN 3 AM 2000 2 152 130 131 63 26 21 NaN 1 AN 2000 0 0 1 2 0 0 0 NaN 0 AO 2000 186 999 1003 912 482 312 194 NaN 247 AR 2000 97 278 594 402 419 368 330 NaN 121 AS 2000 NaN NaN NaN NaN 1 1 NaN NaN NaN

In order to tidy this dataset, we need to remove the different values from the header and unpivot them into rows. We’ll first need to melt the sex + age group columns into a single one. Once we have that single column, we’ll derive three columns from it: sex , age_lower and age_upper . With those, we’ll be able to properly build a tidy dataset.

df = pd . melt ( df , id_vars = [ "country" , "year" ], value_name = "cases" , var_name = "sex_and_age" ) # Extract Sex, Age lower bound and Age upper bound group tmp_df = df [ "sex_and_age" ] . str . extract ( "( \ D)( \ d+)( \ d{2})" ) # Name columns tmp_df . columns = [ "sex" , "age_lower" , "age_upper" ] # Create `age`column based on `age_lower` and `age_upper` tmp_df [ "age" ] = tmp_df [ "age_lower" ] + "-" + tmp_df [ "age_upper" ] # Merge df = pd . concat ([ df , tmp_df ], axis = 1 ) # Drop unnecessary columns and rows df = df . drop ([ 'sex_and_age' , "age_lower" , "age_upper" ], axis = 1 ) df = df . dropna () df = df . sort ( ascending = True , columns = [ "country" , "year" , "sex" , "age" ]) df . head ( 10 )

This results in a tidy dataset.

country year cases sex age AD 2000 0 m 0-14 AD 2000 0 m 15-24 AD 2000 1 m 25-34 AD 2000 0 m 35-44 AD 2000 0 m 45-54 AD 2000 0 m 55-64 AE 2000 3 f 0-14 AE 2000 2 m 0-14 AE 2000 4 m 15-24 AE 2000 4 m 25-34

Variables are stored in both rows and columns

Global Historical Climatology Network Dataset

This dataset represents the daily weather records for a weather station (MX17004) in Mexico for five months in 2010.

Problems:

Variables are stored in both rows ( tmin , tmax ) and columns ( days ).

df = pd . read_csv ( "./data/weather-raw.csv" ) df

id year month element d1 d2 d3 d4 d5 d6 d7 d8 MX17004 2010 1 tmax NaN NaN NaN NaN NaN NaN NaN NaN MX17004 2010 1 tmin NaN NaN NaN NaN NaN NaN NaN NaN MX17004 2010 2 tmax NaN 27.3 24.1 NaN NaN NaN NaN NaN MX17004 2010 2 tmin NaN 14.4 14.4 NaN NaN NaN NaN NaN MX17004 2010 3 tmax NaN NaN NaN NaN 32.1 NaN NaN NaN MX17004 2010 3 tmin NaN NaN NaN NaN 14.2 NaN NaN NaN MX17004 2010 4 tmax NaN NaN NaN NaN NaN NaN NaN NaN MX17004 2010 4 tmin NaN NaN NaN NaN NaN NaN NaN NaN MX17004 2010 5 tmax NaN NaN NaN NaN NaN NaN NaN NaN MX17004 2010 5 tmin NaN NaN NaN NaN NaN NaN NaN NaN

In order to make this dataset tidy, we want to move the three misplaced variables ( tmin , tmax and days ) as three individual columns: tmin . tmax and date .

# Extracting day df [ "day" ] = df [ "day_raw" ] . str . extract ( "d( \ d+)" , expand = False ) df [ "id" ] = "MX17004" # To numeric values df [[ "year" , "month" , "day" ]] = df [[ "year" , "month" , "day" ]] . apply ( lambda x : pd . to_numeric ( x , errors = 'ignore' )) # Creating a date from the different columns def create_date_from_year_month_day ( row ): return datetime . datetime ( year = row [ "year" ], month = int ( row [ "month" ]), day = row [ "day" ]) df [ "date" ] = df . apply ( lambda row : create_date_from_year_month_day ( row ), axis = 1 ) df = df . drop ([ 'year' , "month" , "day" , "day_raw" ], axis = 1 ) df = df . dropna () # Unmelting column "element" df = df . pivot_table ( index = [ "id" , "date" ], columns = "element" , values = "value" ) df . reset_index ( drop = False , inplace = True ) df

id date tmax tmin MX17004 2010-02-02 27.3 14.4 MX17004 2010-02-03 24.1 14.4 MX17004 2010-03-05 32.1 14.2

One type in multiple tables

Dataset: Illinois Male Baby Names for the year 2014/2015.

Problems:

The data is spread across multiple tables/files.

The “Year” variable is present in the file name.

In order to load those different files into a single DataFrame, we can run a custom script that will append the files together. Furthermore, we’ll need to extract the “Year” variable from the file name.

def extract_year ( string ): match = re . match ( ".+( \ d{4})" , string ) if match != None : return match . group ( 1 ) path = './data' allFiles = glob . glob ( path + "/201*-baby-names-illinois.csv" ) frame = pd . DataFrame () df_list = [] for file_ in allFiles : df = pd . read_csv ( file_ , index_col = None , header = 0 ) df . columns = map ( str . lower , df . columns ) df [ "year" ] = extract_year ( file_ ) df_list . append ( df ) df = pd . concat ( df_list ) df . head ( 5 )

rank name frequency sex year 1 Noah 837 Male 2014 2 Alexander 747 Male 2014 3 William 687 Male 2014 4 Michael 680 Male 2014 5 Liam 670 Male 2014

Final Thoughts

In this post, I focused on one aspect of Wickham’s paper, the data manipulation part. My main goal was to demonstrate the data manipulations in Python. It’s important to mention that there is a significant section of his paper that covers the tools and visualizations from which you can benefit by tidying your dataset. I did not cover those in this post.

Overall, I enjoyed preparing this post and wrangling the datasets into a streamlined format. The defined format makes it easier to query and filter the data. This approach makes it easier to reuse libraries and code across analysis. It also makes it easier to share a dataset with other data analysts.