joypy 0.2.6

Python plotting package

Functions for plotting area-proportional two- and three-way Venn diagrams in matplotlib.

Interactive plotting for the Jupyter notebook, using d3.js and ipywidgets.

Statistical data visualization

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

#using numpy's randint to generate some data
df1 = pd.DataFrame(np.random.randint(0,100,size=(10, 2)), columns=list('XY'))
df2 = pd.DataFrame(np.random.randint(0,100,size=(10, 2)), columns=list('XY'))
df3 = pd.DataFrame(np.random.randint(0,100,size=(10, 2)), columns=list('XY'))

df1.head(), df2.head(), df3.head()

(    X   Y
 0  82  32
 1  79  13
 2  87  19
 3   6  73
 4   1  38,
     X   Y
 0  47  62
 1  41   0
 2  98  78
 3  63  83
 4  31  59,
     X   Y
 0  57  25
 1  49  27
 2   9  29
 3  93  75
 4  23  80)

# Get handle of first figure to pass to other plot() calls as ax
ax =  df1.plot(color="red", figsize=(10,5))
df2.plot(ax=ax, color="orange", figsize=(10,5))
df3.plot(ax=ax, color="green", figsize=(10,5))

# Note: subsequent plots need to happen in the same notebook cell.

<AxesSubplot:>

Utilities for the visualization, and visual analysis, of financial data

In this example Python code, we use requests to fetch data from the EIA.gov website and json from the Python standard library to parse the json data. Next, we load the daily prices into a Pandas DataFrame and format the date column and set it as the index. Finally, we use Pandas .plot() to create a chart saving it as a png image.

Datasette plugin providing data dashboards from metadata

A python package for visualizing and manipulating high-dimensional data