#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')


# # Scatterplot with Categories

# A function to quickly produce a scatter plot colored by categories from a pandas `DataFrame` or NumPy `ndarray` object.

# > from mlxtend.general_plotting import category_scatter

# ## Overview

# ### References
# 
# - -

# ## Example 1 - Category Scatter from Pandas DataFrames

# In[2]:


import pandas as pd
from io import StringIO

csvfile = """label,x,y
class1,10.0,8.04
class1,10.5,7.30
class2,8.3,5.5
class2,8.1,5.9
class3,3.5,3.5
class3,3.8,5.1"""

df = pd.read_csv(StringIO(csvfile))
df


# Plotting the data where the categories are determined by the unique values in the label column `label_col`. The `x` and `y` values are simply the column names of the DataFrame that we want to plot.

# In[3]:


import matplotlib.pyplot as plt
from mlxtend.plotting import category_scatter

fig = category_scatter(x='x', y='y', label_col='label', 
                       data=df, legend_loc='upper left')


# ## Example 2 - Category Scatter from NumPy Arrays

# In[4]:


import numpy as np
from io import BytesIO

csvfile = """1,10.0,8.04
1,10.5,7.30
2,8.3,5.5
2,8.1,5.9
3,3.5,3.5
3,3.8,5.1"""

ary = np.genfromtxt(BytesIO(csvfile.encode()), delimiter=',')
ary


# Now, pretending that the first column represents the labels, and the second and third column represent the `x` and `y` values, respectively.

# In[5]:


import matplotlib.pyplot as plt
from mlxtend.plotting import category_scatter

fix = category_scatter(x=1, y=2, label_col=0, 
                       data=ary, legend_loc='upper left')


# ## API

# In[6]:


with open('../../api_modules/mlxtend.plotting/category_scatter.md', 'r') as f:
    print(f.read())