!conda env list

# conda environments:
#
base                     /home/rajaraman/miniconda3
app-ml-services          /home/rajaraman/miniconda3/envs/app-ml-services
evidently312             /home/rajaraman/miniconda3/envs/evidently312
f2                       /home/rajaraman/miniconda3/envs/f2
mai311                   /home/rajaraman/miniconda3/envs/mai311
ml310                    /home/rajaraman/miniconda3/envs/ml310
ml3105                   /home/rajaraman/miniconda3/envs/ml3105
ml311                    /home/rajaraman/miniconda3/envs …

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

size = 0.3
vals = np.array([[60, 32], [37, 40]])

cmap = plt …

# Libraries
import matplotlib.pyplot as plt

# Make data: I have 3 groups and 7 subgroups
group_names=['groupA', 'groupB', 'groupC']
group_size=[12,11,30]
subgroup_names=['A.1', 'A.2', 'A …

import pandas as pd
import numpy as np

abc = np.array([
    [9, 13, 10],
    [7, 12, 9],
    [19, 11, 8],
    [12, 10, 11]
])

abc

array([[ 9, 13, 10],
       [ 7, 12,  9],
       [19, 11,  8],
       [12, 10 …

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(13)
arr_data = np.random.normal(0,1, 100)

yhist, binedges = np.histogram(arr_data)

bincenters = np.mean(np.vstack([binedges[0:-1],binedges[1:]]), axis=0 …

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5, 6, 7]

y = [
    0,
    220,
    380,
    500,
    540,
    540,
    590,
]

y

[0, 220, 380, 500, 540, 540, 590]

plt.plot(x, y, 'ro')

# plt.axis([0, 10, 20, 100])

[<matplotlib.lines.Line2D at 0x7f895c338c20>]

import pandas as pd
import numpy as np

from plotnine import *

%matplotlib inline

df = pd.DataFrame({
    'variable': ['gender', 'gender', 'age', 'age', 'age', 'income', 'income', 'income', 'income'],
    'category': ['Female', 'Male', '1-24', '25-54', '55+', 'Lo', 'Lo-Med', 'Med', 'High …

import matplotlib.pyplot as plt
import pandas as pd
from math import pi

df = pd.DataFrame({
    'group' : ['A', 'B', 'C', 'D'],

    'var1' : [10, 20, 30, 4],
    'var2' : [2, 88, 22, 2]
})

df

# copied from https://python-graph-gallery.com/390-basic-radar-chart/

# Libraries
import matplotlib.pyplot as plt
import pandas as pd
from math import pi

# Set data
df = pd.DataFrame({
'group': ['A','B','C','D'],

'java': [38, 1.5, 30, 4 …

import numpy as np
import matplotlib.pylab as plt

a = np.random.randint(10, size=20)

a

array([2, 5, 5, 4, 9, 0, 4, 9, 0, 1, 9, 7, 9, 7, 1, 5, 7 …