When creating some really great visualizations, I wondered how I could create a bunch of them and not be overwhelmed by the process of exporting them to PDF files to share with others. So I explored a few options and found that img2pdf was most suitable: always lossless, small, and fast. It allows me to loop through my data, creating charts, move them to individual PDFs, and then combine them into multi-page PDFs.
I’m using DataLab by DataCamp, where I’m learning Python, Data Science, and AI. So, some of my code may rely on that environment and YMMV. Installing and importing img2pdf was very straightforward for me.
!pip install img2pdf
import img2pdf
It turned out to be pretty simple to loop through my CSV data using one of the columns to get data by player and then create each graph, saving it as a PDF, then combining then as multi-page PDFs by player and by category.
I created a directory structure for the files, with a Fig Storage directory for all the individual PDFs and a directory for each team and year. This allows me to scale it to handle data at volume, letting me focus on analyzing that data instead of being bogged down in copying and pasting.
Within each loop, it creates an empty array, imagefiles, in which all filenames are placed, so that those files can be copied into the summary PDFs once the charts have all been generated. Outside the loop, there is another array, byDateArrayFiles, for storing all of the filenames to be bundled together for a ‘Velocity by Date’ file.
Here’s a sample of the loop with only two charts created. I have 8 different ones created for each player, but that would be excessive. This gives you the idea.
season = '2025'
team = 'ICI'
byDateArrayFiles = []
playerNames = pitchLogic['Player Name'].unique()
for player in playerNames:
player_df = pitchLogic[pitchLogic['Player Name'] == player]
imagefiles = []
bydatefig, bydateax = plt.subplots()
bydateax = sns.lineplot(x='Date', y='Speed', data=player_df).set(title='Velocity for ' + player)
bydatefig.autofmt_xdate(rotation=75)
filename = "Fig Storage/" + player + ' VelocityByDate.jpg'
bydatefig.savefig(filename)
byDateArrayFiles.append(filename)
imagefiles.append(filename)
only100_player_df = player_df[abs(player_df['Slot Diff'])<=100]
only100_player_df.loc[only100_player_df['Location'] != 'K', 'Location'] = 'BB'
slotfig = sns.relplot(x='Horiz Mvmt', y='Vertical Mvmt', data=only100_player_df, kind='scatter', hue='Slot Diff', size='Location', style='Type').set(title='Slot Difference and Movement Profile for ' + player)
filename = "2025 Samples/" + player + ' SlotMovement.jpg'
slotfig.savefig(filename)
imagefiles.append(filename)
with open(season + " " + team + "/" + player + ".pdf", "wb") as pdf_file:
pdf_file.write(img2pdf.convert(imagefiles))
with open(season + " " + team + "/Velocity By Date.pdf", "wb") as pdf_file:
pdf_file.write(img2pdf.convert(byDateArrayFiles))
This all saved me loads of time and headaches generating the charts. It lets me quickly explore whether my visualizations are meaningful. It also makes modifying them or updating them very easy. I can put a season’s worth of pitches into the system and have a suite of charts for each player a minute later.
Lost in Code 