pandas - read CSV, convert columns, calculate average, plot bar chart
pandas - read CSV, convert columns, calculate average, plot bar chart
I had speedtest-cli running on a regular basis to figure out which provider mobile provider works best in a given location. For this, I gathered all speedtest results in a CSV and just wanted to get the average download and upload speed over all entries.
This is how the CSV looks like:
| server name | server id | idle latency | idle jitter | packet loss | download | upload | download bytes | upload bytes | share url | download server count | download latency | download latency jitter | download latency low | download latency high | upload latency | upload latency jitter | upload latency low | upload latency high | idle latency low | idle latency high |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| foobar | 1337 | 33.1252 | 8.44775 | 0 | 1067331 | 436851 | 12296456 | 4025128 | 1 | 737.564 | 85.5956 | 39.424 | 1710.7 | 323.56 | 69.9196 | 35.317 | 1023 | 26.975 | 44.814 |
and here’s the python script how to convert all the things and display them:
import pandas as pd
import matplotlib.pyplot as plt
"""read csv"""
df = pd.read_csv('results.csv')
"""convert bytes to mbps"""
def bytes_to_mbits(num_bytes):
return num_bytes * 8 / 1e6
"""take just the download and upload columns from the csv file, convert each entries using the function above, round to two decimal points"""
dl_ul = df[['download', 'upload']]
dl_ul.reset_index(drop=True)
download = round(df['download'].apply(bytes_to_mbits), 2)
upload = round(df['upload'].apply(bytes_to_mbits), 2)
"""take the download and upload results, calculate average, put on bar chart"""
plt.bar(['download', 'upload'], [download.mean(), upload.mean()], color=['green', 'red'])
plt.ylabel('speed in mbit/s')
plt.title('speedtest')
plt.show()