Two or more pandas columns on the same seaborn scatterplot

Question

I am trying to do a scatter plot for the following data with all columns in one plot.

Actually I imported this data from csv file and saved in a dataframe df_inv and then I saved it in variable tips

tips = df_inv
sns.scatterplot(data=tips, x=df_inv.index, y = "a")
plt.show()

I want to add columns b, c, and d on the same plot but I am unable to find the right code. I have tried y = ["a", "b", "c", "d", "e"] but it didn't worked. I want my result in the following format ideally not all circles but some x, *, and other shapes.

Answer 1

You could re-shape your data in a different dataframe with pandas.melt:

df_inv = df_inv.reset_index()
columns = ['index', 'a', 'b', 'c', 'd']
df_to_plot = df_inv[columns]

df_to_plot = pd.melt(frame = df_to_plot,
                     id_vars = 'index',
                     var_name = 'column_name',
                     value_name = 'value')

In this way, you will get something like:

    index column_name  value
0       0           a    315
1       1           a    175
2       2           a     65
3       3           a    370
4       4           a    419
5       0           b    173
6       1           b    206
7       2           b    271
8       3           b    463
9       4           b    419
10      0           c     58
...

Now you can finally plot with a single line of code:

sns.scatterplot(ax = ax, data = df_to_plot, x = 'index', y = 'value', style = 'column_name', hue = 'column_name')

Complete code

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns


N = 5
df_inv = pd.DataFrame()
df_inv['a'] = np.random.randint(low = 50, high = 500, size = N)
df_inv['b'] = np.random.randint(low = 50, high = 500, size = N)
df_inv['c'] = np.random.randint(low = 50, high = 500, size = N)
df_inv['d'] = np.random.randint(low = 50, high = 500, size = N)


df_inv = df_inv.reset_index()
columns = ['index', 'a', 'b', 'c', 'd']
df_to_plot = df_inv[columns]

df_to_plot = pd.melt(frame = df_to_plot,
                     id_vars = 'index',
                     var_name = 'column_name',
                     value_name = 'value')


fig, ax = plt.subplots()

sns.scatterplot(ax = ax, data = df_to_plot, x = 'index', y = 'value', style = 'column_name', hue = 'column_name')

plt.show()