Is there a way to show more columns per row from FastF1 (see code)-CodePudding

I am very new to coding and I'm trying to view data from FastF1. I am attempting to do this using Python and Jupyter Lab. Whenever I print two columns using the code below:

import fastf1
from fastf1 import plotting
import pandas as pd

plotting.setup_mpl()

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)

fp3 = fastf1.get_session(2022,'Monza','FP3')fp3.load()
fp3_laps = fp3.laps
fp3_d1 = fp3_laps.loc[fp3_laps['Driver']=='VER']
print([[fp3_d1.LapTime,  fp3_d1.LapNumber]])

My aim would be to get have both the LapTime and LapNumber columns next to each other.

CodePudding user response：

fp3 = fastf1.get_session(2022,'Monza','FP3')fp3.load()
fp3_laps = fp3.laps
fp3_d1 = fp3_laps.loc[fp3_laps['Driver']=='VER']
print([[fp3_d1.LapTime,  fp3_d1.LapNumber]])

From the above code, it looks like fp3_d1 is a dataframe. So fp3_d1.LapTime is a pandas series, in other words, selecting a column from the dataframe. It's the same as fp3_d1['LapTime'] (I prefer this notation). You can check with type(fp3_d1['LapTime']) to get "pandas.core.series.Series".

And fp3_d1.LapNumber (the same as fp3_d1['LapNumber']) is another pandas series. The last line of the code is simply to stack 2 pandas series vertically, becoming "a list of 1 list of 2 items", notice the double square brackets [[ in front and behind ]] and a comma , in between.

The syntax df[['col1', 'col2']] is to extract 2 columns from the dataframe, forming a subset of the full dataframe with only the columns col1 and col2 side by side. You can check with df[['col1', 'col2']] to get "pandas.core.frame.DataFrame".

Your aim to get both LapTime and LapNumber columns next to each other, is to get a subset of the dataframe with these columns. So the code should be:

print(fp3_d1[['LapTime', 'LapNumber']])

CodePudding user response：

You can do dataframe subsetting like fp3_d1[['LapTime', 'LapNumber']]. This will output a subset of the dataframe with the 2 columns indicated.

Refer to pandas documentation for more details