I have a dataframe that looks like the following
color x y
0 red 0 0
1 red 1 1
2 red 2 2
3 red 3 3
4 red 4 4
5 red 5 5
6 red 6 6
7 red 7 7
8 red 8 8
9 red 9 9
10 blue 0 0
11 blue 1 1
12 blue 2 4
13 blue 3 9
14 blue 4 16
15 blue 5 25
16 blue 6 36
17 blue 7 49
18 blue 8 64
19 blue 9 81
I ultimately want two lines, one blue, one red. The red line should essentially be y=x and the blue line should be y=x^2
When I do the following:
df.plot(x='x', y='y')
The output is this:
Is there a way to make pandas know that there are two sets? And group them accordingly. I'd like to be able to specify the column color
as the set differentiator
You can plot multiple lines from the data provided by a Dataframe in python using matplotlib. You can do it by specifying different columns of the dataframe as the x and y-axis parameters in the matplotlib. pyplot. plot() function.
In Matplotlib, we can draw multiple graphs in a single plot in two ways. One is by using subplot() function and other by superimposition of second graph on the first i.e, all graphs will appear on the same plot.
You probably need to re-organize your dataframe in a suitable way so that there is one column for the x data, one for the y data, and one which holds the label for the data point. You can also just use matplotlib. pyplot . If you import seaborn , much of the improved design is also used for "regular" matplotlib plots.
Another simple way is to use the pandas.DataFrame.pivot
function to format the data.
Use pandas.DataFrame.plot
to plot. Providing the colors in the 'color'
column exist in matplotlib: List of named colors, they can be passed to the color
parameter.
# sample data
df = pd.DataFrame([['red', 0, 0], ['red', 1, 1], ['red', 2, 2], ['red', 3, 3], ['red', 4, 4], ['red', 5, 5], ['red', 6, 6], ['red', 7, 7], ['red', 8, 8], ['red', 9, 9], ['blue', 0, 0], ['blue', 1, 1], ['blue', 2, 4], ['blue', 3, 9], ['blue', 4, 16], ['blue', 5, 25], ['blue', 6, 36], ['blue', 7, 49], ['blue', 8, 64], ['blue', 9, 81]], columns=['color', 'x', 'y'])
# pivot the data into the correct shape
df = df.pivot(index='x', columns='color', values='y')
# display(df)
color blue red
x
0 0 0
1 1 1
2 4 2
3 9 3
4 16 4
5 25 5
6 36 6
7 49 7
8 64 8
9 81 9
# plot the pivoted dataframe; if the column names aren't colors, remove color=df.columns
df.plot(color=df.columns, figsize=(5, 3))
You could use groupby
to split the DataFrame into subgroups according to the color:
for key, grp in df.groupby(['color']):
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_table('data', sep='\s+')
fig, ax = plt.subplots()
for key, grp in df.groupby(['color']):
ax = grp.plot(ax=ax, kind='line', x='x', y='y', c=key, label=key)
plt.legend(loc='best')
plt.show()
yields
If you have seaborn
installed, an easier method that does not require you to perform pivot
:
import seaborn as sns
sns.lineplot(data=df, x='x', y='y', hue='color')
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