Creating multiple lines in plot grouped by selected column of larger dataframe

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I have a dataframe, example sample below:

import datetime
import pandas as pd

ids =[1, 2, 3, 1, 2, 3]
vals = [3, 5, 6, 3, 7, 8]
lats = [10, 10, 10, 30, 30, 30]
ratio = [.1, .4, .2, .3, .4, .5,]

df = pd.DataFrame({'ids' : ids, 'vals' : vals, 'lats' : lats, 'ratio' : ratio})


>>>df
    ids vals    lats    ratio
0   1   3       10      0.1
1   2   5       10      0.4
2   3   6       10      0.2
3   1   3       30      0.3
4   2   7       30      0.4
5   3   8       30      0.5

I want to create a graph with lines that have ratio on the y-axis, lats on the x-axis and are grouped by the ids column. All the questions I've found use groupby or pivot on a dataframe that is used fully, and not a selection of columns.

I need to make more graphs on my true dataframe, which has many more columns and therefore would like to know how to plot this by selecting specified columns.

dataframe matplotlib python
1 Answers

You can use the grouby function follow by a for loop, then, use the plot function for each of the groups, passing the desired columns as x and y (in this particular order, if you wish to maintain the described plot).

import matplotlib.pyplot as plt
...
...

x_axis = 'lats'  # specified columns
y_axis = 'ratio' # specified columns

groups = df.groupby('ids')
for n,g in groups:
    plt.plot(g[x_axis], g[y_axis], label=f'ID-{n}')

plt.xlabel(x_axis.capitalize())
plt.ylabel(y_axis.capitalize())
plt.legend()
plt.grid(True)
plt.show()

Another way of plot Pandas dataframes columns is passing the data argument to the plot function and the name of the columns as strings:

Instead of giving the data in x and y, you can provide the object in the data parameter and just give the labels for x and y

But here, you would still have to pass the dataframe group on each iteration

plt.plot('lats', 'ratio', data=g)

grouped_lines

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