Learning a new coding language is always a challenge. One thing that helps me is to create a short tutorial for myself of some of the basic data tasks that I might do when I initially sit down with a data set. I try and think through this in relationship to stuff I might have done (a long, long time ago) in Excel with regards to summarizing data, adding new features, and creating pivot tables.

Since I’m not that great in Python, here is my Doing things in Python that you would normally do in Excel tutorial that may help others looking to get started with this coding language.

The tasks that I cover are:

1. Exploring features of the data
2. Sorting the columns
3. Filtering the columns
4. Creating new features
5. Calculating summary statistics
6. Building pivot tables
7. Data visualization

The data I use comes form the pybaseball library, freely available for install in python. I’ll be using the pitchers dataset from years 2012 to 2016.

The entire jupyter notebook is accessible on my GitHub page.

Libraries and Data

The libraries I use are:

• pandas — for working with data frames
• numpy — for additional computational support
• matplotlib & seaborn — for plotting data
• pybaseball — for data acquisition

I called the data set pitchers. The data consists of 408 rows and 334 columns. After doing a bit of exploring the data set (seeing how large it is, checking columns for NA values, etc), we begin by sorting the columns.

Sort Columns

Sorting columns is done by calling the name of the data set and using the sort_values() function, passing it the column you’d like to sort on (in this case, sorting alphabetically by pitcher name)

## Sort the data by pitcher name

pitchers.sort_values(by='Name')

If you have a specific direction you’d like to sort by, set the ‘ascending’ argument to either True or False. In this case, setting ascending = False allows us to sort the ERA from highest to lowest (descending order).

## Sort the data by ERA from highest to lowest

pitchers.sort_values(by = 'ERA', ascending = False)

(For additional sorting examples, see the GitHub code)

Filtering Columns

Filtering can be performed by explicitly stating the value you’d like to filter on within the square brackets. Here, I call the data frame (pitchers) and add the .loc function after the data frame name in order to access the rows that are specific to the condition of interest. Here, I’m only interested in looking at the 2012 season. Additionally, I only want a few columns (rather than the 334 from the full data set). As such, I specify those columns AFTER the comma within the square brackets. Everything to the left of the comma is specific to the rows I want to filter (Season == 2012) and everything to the right of the comma represents the columns of interest I’d like returned.

## Filter to only see the 2012 season
# Keep only columns: Name, Team, Age, G, W, L, WAR, and ERA

season2012 = pitchers.loc[pitchers['Season']== 2012, ['Name', 'Team', 'Age', 'G', 'W', 'L', 'WAR', 'ERA']]
season2012.head()

If I only want to look at Clayton Kershaw over this time period, I can filter him out like so:

## Filter Clayton Kershaw's seasons
# Keep only columns: Season, Name, Team, Age, G, W, L, WAR, and ERA
# arrange the data set from earliest season to latest

kershaw = pitchers.loc[pitchers['Name']=='Clayton Kershaw', ['Season', 'Name', 'Team', 'Age', 'G', 'W', 'L', 'WAR', 'ERA']].sort_values('Season', ascending = True)
kershaw.head()

To make the data set more palatable for the rest of the tutorial, I’m going to create a smaller data set, with fewer columns (pitchers_small).

## Create a smaller data set
# Keep only columns: Season, Name, Team, Age, G, W, L, WAR, ERA, Start-IP, and Relief-IP
# arrange the data set from earliest season to latest for each pitcher

pitchers_small = pitchers[['Season', 'Name', 'Team', 'Age', 'G', 'W', 'L', 'WAR', 'ERA', 'Start-IP','Relief-IP']].sort_values(['Name', 'Season'], ascending = True)
pitchers_small.head(30)

Creating New Features

I create three new features in the data set:

1. A sequence counter that counts the season number of each pitcher from 1 to N seasons that they are in the data set.This is done by simply grouping the data by the pitcher name and then cumulatively counting each row that the pitcher is seen in the data. Notice I add “+1” to the end of the code because python begins counter at “0”. NOTE: To make this work properly, ensure that the data is ordered by pitcher name and season. I did this at the end of my code, in the previous step.
2. An ‘age group’ feature that groups the ages of the pitchers in 5 year bins.To accomplish this task, I use a 3 step process. First, I specify where I want the age bins to occur and assign it to the bins variable. I then create the labels I would like to correspond to each of the bins and assign that to the age_group variable. Then I use the np.select() function to combine this information, assigning it to a new column in my data set called ‘age_group‘.
3. A ‘pitcher type’ feature that considers anyone who’s starter innings pitched was greater or equal to the median number of starting innings pitched as a ‘starter’ and all others as ‘relievers’.To create the pitcher_type column, I use the np.where() function, which works like ifelse() or case_when() in R or like IF() in excel. The first argument is the condition I’d like checked (“did this pitcher have starter innings pitched that were greater than or equal to the median number of starter innings pitched?). If the condition is met, the function will assign the pitcher in that row as a “starter”. If the condition is not met, then the pitcher in that row is designated as a “reliever”.

 

## Add a sequence counter for each season for each pitcher
pitchers_small['season_id'] = pitchers_small.groupby(['Name']).cumcount() + 1

## Create a new column called 'age_group'
# create conditions for the age_group bins
bins = [
    (pitchers_small['Age'] <= 25), (pitchers_small['Age'] > 25) & (pitchers_small['Age'] <= 30), (pitchers_small['Age'] > 30) & (pitchers_small['Age'] <= 35), (pitchers_small['Age'] > 35) & (pitchers_small['Age'] <= 40), (pitchers_small['Age'] > 40)
]

# create the age_group names to be assigned to each bin
age_group = ['<= 25', '25 to 30', '31 to 35', '36 to 40', '> 40']

# add the age_group bins into the data
pitchers_small['age_group'] = np.select(bins, age_group)

## Create a pitcher_type column which makes a distinction between starters and relievers
pitchers_small['pitcher_type'] = np.where(pitchers_small['Start-IP'] >= pitchers_small['Start-IP'].median(), 'starter', 'reliever') 

pitchers_small.head()

Calculating Summary Statistics

The GItHub repo for this post offers a few ways of obtaining the mean, standard deviation, and counts of values for different columns. For simplicity, I’ll show a convenient way to get summary stats over an entire data set using the describe() function, which is called following the name of the data frame and a period.

## Get summary stats for each column

pitchers_small.describe()

Pivot Tables

There are a few ways to create a pivot table in python. One way is to use the groupby() function for the class you’d like to summarize over and then call the mathematical operation (e.g., mean) you are interested in. I have an example of this in the GitHub post in code chuck 42 as well as several examples of other pivot table options. Another way is to use the pivot_table() function from the pandas library.

Below is a pivot table of the mean and standard deviation of pitcher age across the 5 seasons in the data set.

## Pivot Table of Average and Standard Deviation of Wins by Season
# Round the results to 1 significant digit

round(pitchers_small.pivot_table(values = ['Age'], index = ['Season'], aggfunc = (np.mean, np.std)), ndigits = 1)

You can also make more complicated pivot tables by setting the columns to a second grouping variable, as one would do in Excel. Below, we look at the average WAR across all 5 seasons within the 5 age groups (which we created in the previous section).

## Calculate the average WAR per season across age group

pitchers_small.pivot_table(values = ['WAR'], index = ['Season'], columns = ['age_group'], aggfunc = np.mean)

Data Visualization

In the GitHub post I walk through how to plot 8 different plots:

1. Histogram
2. Density plots by group
3. Boxplots by group
4. Scatter plot
5. Scatter plot highlighting groups
6. Bar plots for counting values
7. Line plot for a single player over time
8. Line plots for multiple players over time

 

Feedback from the last two blog entries suggests that people seem to enjoy the “how to” nature of them. Walking through how to do some basic coding steps while exploring, summarizing, visualizing, and analyzing data is a great way to help others feel comfortable when trying to move from excel or SPSS to a code language (like R or Python). Additionally, this also helping me to get back to first principles and put together a step-by-step approach to teaching this stuff to others.

As such, I’ve decided to start incorporating some articles where I walk through each line of code. Since I’ve started to do a bit more work in Python (though I still strongly favor R), I feel like this is a great place for me to start.

For those of us who started out in sport and exercise science, using Excel Pivot Tables was one of the first way we began to explore our data and summarize what was there. Today, I’ll walk through how we can create some of those exact same pivot tables in Python.

The Jupyter Notebook and data for this article are available on my GitHub page.

The data I used for this article was taken from www.hockey-reference.com and consists of game outcomes from the 2014-2015 Season through the 2018-2019 Season. I stored the data as a .csv file for this article but I’ll do a future blog article on how to scrape data from webpages like this. This is additionally fun because I know very little about hockey, so I get to learn along the way!

Step 1: Load Packages

• First I start out just loading the packages (Python people call them “libraries” but I’m still stuck in the old R jargon, so I say “packages”) that contain some of the functions that I’ll need to do my analysis.
• Pandas is the main data frame package in Python and numpy is useful for dealing with arrays and various mathematical functions.
• Seaborn and Matplotlib are two packages that I mainly use for data visualizing, however I do not do anything visualization in this article.
• Finally, the OS package is what I use to check my working directory and change the working directory, if needed (more on that later).
• Once importing each package I give them an alias (e.g., pandas as pd) so that I can use a short hand version of the package name when I call one of it’s functions, which you will see throughout this article.

# Load packages

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os as os

Step 2: Change the working directory if needed and load data

• The working directory is the place where the data for your analysis is stored.
• os.getcwd() is a function from the “os” package that gets my current working directory. If the directory needs to be changed to a different folder (for example, if the data were stored somewhere other than my desktop), I use the os.chdir() function to change it.
• Once I have the directory set, I load the data using the pd.read_csv() function from the pandas package.
• After the data is loaded, we take a look at the first few rows of the data frame.
• An example of all of these steps is provided in the screen shot of my Jupyter notebook.

# Get current working directory
os.getcwd()

# Change the working directory
os.chdir('/Users/Patrick/Desktop')

# Load the 2014 - 2018 NHL Scores data
nhl = pd.read_csv('2014-2018 NHL Scores.csv')

# Look at the first few rows
nhl.head()

Step 3: Clean up the data (All little housekeeping)

• We notice that the Goals (‘G’) column is repeated, once for the Visitor and once for the Home team. Thus, the second time it is referred to as ‘G.1’. So, we can rename these columns to ‘Visitor_G’ and ‘Home_G’, so that they are more explicit and less confusing.
• Next, we want to create four new columns. First, we create a column that calculates the point differential of the game for the home team. Then we create a column that identifies whether the winner was the home or away team. Finally, we create a column that tells us which team was the winner and a column telling us which team was the loser. You can see that I do all of these steps by first creating the conditions I’m testing (comparing goals for the home team to goals for the away team) and then assigning the outcome a specific label (e.g. home, away, or tie).

nhl['Point_Diff'] = nhl['Home_G'] - nhl['Visitor_G']

conditions = [
(nhl['Home_G'] > nhl['Visitor_G']),
(nhl['Home_G'] < nhl['Visitor_G']),
(nhl['Home_G'] == nhl['Visitor_G'])]

choices = ['home', 'visitor', 'tie']

nhl['winner'] = np.select(conditions, choices, default = 'null')

cond_for_winner = [
(nhl['winner'] == 'home'),
(nhl['winner'] == 'visitor'),
(nhl['winner'] == 'tie')]

choices_for_winner = [nhl['Home'], nhl['Visitor'], 'tie']
choices_for_loser = [nhl['Visitor'], nhl['Home'], 'tie']

nhl['winning_team'] = np.select(cond_for_winner, choices_for_winner, default = 'null')
nhl['losing_team'] = np.select(cond_for_winner, choices_for_loser, default = 'null')

• Our data frame now looks like this:

Step 4: Create Pivot Tables

Now that we have the data arranged, we can create some pivot tables.

First, let’s create a pivot table looking at the average home point differential by season.

nhl.groupby('Season').mean()['Point_Diff']

 

I produced this, as you can see from the code, by looking for the mean of ‘Point_Diff’, grouped by the variable ‘Season’.

This is useful, but I don’t love how it looks. Using the pivot_table() function, we can get a nicer looking output.

nhl.pivot_table(values = 'Point_Diff', index = 'Season', aggfunc = np.mean)

 

Much nicer!!

Next, lets look at how many times the home team won.

nhl.groupby(['winner']).size()

If we divide by the total number of observations, we get the percentage of times the home team won.

nhl.groupby(['winner']).size() / len(nhl.index)

 

We can also look at home and visitor win percentage by each season within our data, again by using the groupby() function. We then create two columns, one for home and one for visitor win percentage with just some basic math.

season_totals = nhl.groupby(['Season', 'winner']).size().unstack()
season_totals['Home_Win_Pct'] = season_totals['home'] / (season_totals['home'] + season_totals['visitor'])
season_totals['Visitor_Win_Pct'] = season_totals['visitor'] / (season_totals['home'] + season_totals['visitor'])
season_totals

Finally, we can look at the Win% for each team over this 5 year stretch. Three steps were required to do this:

1. The first chunk of code gets the total number of times each team played a game.
2. The second chunk of code tally’s up the teams total wins and losses and stores them in a data frame (team_perf).
3. The final chunk of code calculates a win percentage an then sorts the win percentage from best to worse.

team_win_totals = nhl.groupby(['winning_team']).size()
team_loss_totals = nhl.groupby(['losing_team']).size()

team_win_totals = pd.DataFrame(team_win_totals, columns = ['wins'])
team_loss_totals = pd.DataFrame(team_loss_totals, columns = ['losses'])
team_perf = team_win_totals.join(team_loss_totals)

team_perf['Win_Pct'] = team_perf['wins'] / (team_perf['wins'] + team_perf['losses'])
team_perf.sort_values(by = ['Win_Pct'], ascending = False)

Conclusion

There are a number of other things we could have built Pivot Tables on to explore this simple data set. Feel free to take the code and the data from my GitHub page and play around with it yourself. I don’t code too often in Python (still trying to figure out a lot of the syntax) so if you notice any errors in the code or ways to make it cleaner (nothing beats R Tidyverse, in my opinion), feel free to comment below.