How Do You Graph Multiple Items In A Dataframe On One Graph Using Pandas And Matplotlib.pyplot?

The dataframe I am trying to graph is below. I want to plot each fieldname as the legend item with x=year and y=value The name of the dataframe is my_gross fie

Solution 1:

The standard pandas and matplotlib approach is to pivot to wide-form and plot:

import pandas as pd
from matplotlib import pyplot as plt

plot_df = df.pivot(index='year',
                   columns='fieldName',
                   values='value')

plot_df.plot()
plt.tight_layout()
plt.show()

plot_df:

fieldNamediluted_shares_outstandingepssales_revenueyear2015                       96379000.07.360470e+085.817000e+092016                       87775000.07.285207e+085.762000e+092017                       85562000.08.944702e+086.641000e+092018                       83530000.01.298734e+098.047000e+092019                       77710000.01.451550e+099.351000e+092020                       72929000.01.259110e+098.530000e+09

---

seaborn.lineplot has built-in functionality with hue without needing to reshape:

import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

sns.lineplot(data=df, x='year', y='value', hue='fieldName')
plt.tight_layout()
plt.show()

Solution 2:

There're several ways to do it, depending on libraries available.

Using just pandas (with matplotlib being used by pandas in backend):

Loop over unique values in your 'fieldName' column, filter the DataFrame to only include that value, set index to year (this will be your x-axis), and choose the value you intent to plot ('value' Series), then plot it.

for fieldname in df['fieldName'].unique():
    df[df['fieldName'] == fieldname].set_index('year')['value'].plot(label = fieldname)
plt.legend()

Outputs:

EDIT:

Seems like a relatively simple groupby works (no loops needed):

df.set_index('year').groupby('fieldName')['value'].plot()
plt.legend()

Outputs: