geoplot 0.5.1

Convert Matplotlib plots into Leaflet web maps

In this blog post, we’ll utilize the powerful libraries Matplotlib, Numpy and Pandas to perform data generation and visualization. We’ll discuss the programming concepts, methods, and functionalities used in this script.

An extension to pandas describe function.

Python client for Twelve Data

A suite of visual analysis and diagnostic tools for machine learning.

Joyplots in python

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.