CODEWITHSUNDEEP
pythonpandasmatplotlibchartsvisualization
Fede2424
Fede2424

Reputation: 77

Create a Radar Chart in python for each row of a Panda dataframe

I am using panda in order to assign a score to some gamers.

I computed, using the same KPIs, some attributes for every gamer and now I have, for each player, a row with the results.

The dataframe looks like this (the only difference is that it has more columns) :

| Name | Speed | ATK |

| G1 | 0.32 | 0.89 |

| G4 | 0.31 | 0.76 |

I thought it would be nice to plot a radar chart (https://en.wikipedia.org/wiki/Radar_chart) for each row using matplotlib (if possible).

How would you do it?

Is there a better alternative to matplotlib?

Thanks.

Upvotes: 1

Views: 1477

Answers (1)

Anwarvic
Anwarvic

Reputation: 12992

To get this spider-look, you need at least three columns. So, I've added a Random column to your dataframe:

import pandas as pd
df = pd.DataFrame({"Name": ["G1", "G4"],
                    "Speed": [0.32, 0.31],
                    "ATK": [0.89, 0.76],
                    "Random": [0.4, 0.8]})
print(df)
#  Name  Speed   ATK  Random
#0   G1   0.32  0.89     0.4
#1   G4   0.31  0.76     0.8

Now, let's see how to plot this simple dataframe. The follwing code is adapted from this blog post:

# import necessary modules
import numpy as np
import matplotlib.pyplot as plt
from math import pi


# obtain df information
categories = list(df)[1:]
values = df.mean().values.flatten().tolist()
values += values[:1] # repeat the first value to close the circular graph
angles = [n / float(len(categories)) * 2 * pi for n in range(len(categories))]
angles += angles[:1]

# define plot
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8),
                        subplot_kw=dict(polar=True))
plt.xticks(angles[:-1], categories, color='grey', size=12)
plt.yticks(np.arange(0.5, 2, 0.5), ['0.5', '1.0', '1.5'],
           color='grey', size=12)
plt.ylim(0, 2)
ax.set_rlabel_position(30)

# draw radar-chart:
for i in range(len(df)):
    val_c1 = df.loc[i].drop('Name').values.flatten().tolist()
    val_c1 += val_c1[:1]
    ax.plot(angles, val_c1, linewidth=1, linestyle='solid',
            label=df.loc[i]["Name"])
    ax.fill(angles, val_c1, alpha=0.4)

# add legent and show plot
plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))
plt.show()

Which results a graph like so: enter image description here

Upvotes: 1

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