pandas-alive 0.2.4

Description:

pandasalive 0.2.4

Pandas_Alive
Animated plotting extension for Pandas with Matplotlib

Pandas_Alive is intended to provide a plotting backend for animated matplotlib charts for Pandas DataFrames, similar to the already existing Visualization feature of Pandas.
With Pandas_Alive, creating stunning, animated visualisations is as easy as calling:
df.plot_animated()

Table of Contents



Installation
Usage
Currently Supported Chart Types

Horizontal Bar Chart Races
Vertical Bar Chart Races
Line Charts
Bar Charts
Scatter Charts
Pie Charts
Bubble Charts

Bubble Chart Example 1
Bubble Chart Example 2


GeoSpatial Charts

GeoSpatial Point Charts
Polygon GeoSpatial Charts




Multiple Charts

Urban Population
Life Expectancy in G7 Countries
NSW COVID Visualisation
Italy COVID Visualisation
Simple Pendulum Motion


HTML 5 Videos
Progress Bars!
Future Features
Tutorials
Inspiration
Requirements
Documentation
Contributing

Development


Changelog


Installation
Install with pip install pandas_alive or conda install pandas_alive -c conda-forge
Usage
As this package was inspired by bar_chart_race, the example data set is sourced from there.
Must begin with a pandas DataFrame containing 'wide' data where:

Every row represents a single period of time
Each column holds the value for a particular category
The index contains the time component (optional)

The data below is an example of properly formatted data. It shows total deaths from COVID-19 for the highest 20 countries by date.

To produce the above visualisation:

Check Requirements first to ensure you have the tooling installed!
Call plot_animated() on the DataFrame

Either specify a file name to write to with df.plot_animated(filename='example.mp4') or use df.plot_animated().get_html5_video to return a HTML5 video


Done!

Note on custom figures in notebooks:
When setting up custom figures for animations in Matplotlib make sure to use the Figure() syntax and not figure() instance type. The latter causes animations in Matplotlib, and in turn in pandas_alive, to take twice as long to be generated when changing from 'Figure' to 'figure' syntax.
More on 'Figure' vs 'figure' can be found in this SO entry, and this other SO entry.
import pandas_alive

covid_df = pandas_alive.load_dataset()

covid_df.plot_animated(filename='examples/example-barh-chart.gif')

Currently Supported Chart Types
Horizontal Bar Chart Races
import pandas as pd
import pandas_alive

elec_df = pd.read_csv("data/Aus_Elec_Gen_1980_2018.csv",index_col=0,parse_dates=[0],thousands=',')

elec_df.fillna(0).plot_animated('examples/example-electricity-generated-australia.gif',period_fmt="%Y",title='Australian Electricity Generation Sources 1980-2018')


import pandas_alive

covid_df = pandas_alive.load_dataset()

def current_total(values):
total = values.sum()
s = f'Total : {int(total)}'
return {'x': .85, 'y': .2, 's': s, 'ha': 'right', 'size': 11}

covid_df.plot_animated(filename='examples/summary-func-example.gif',period_summary_func=current_total)


import pandas as pd
import pandas_alive

elec_df = pd.read_csv("data/Aus_Elec_Gen_1980_2018.csv",index_col=0,parse_dates=[0],thousands=',')

elec_df.fillna(0).plot_animated('examples/fixed-example.gif',period_fmt="%Y",title='Australian Electricity Generation Sources 1980-2018',fixed_max=True,fixed_order=True)


import pandas_alive

covid_df = pandas_alive.load_dataset()

covid_df.plot_animated(filename='examples/perpendicular-example.gif',perpendicular_bar_func='mean')


Vertical Bar Chart Races
import pandas_alive

covid_df = pandas_alive.load_dataset()

covid_df.plot_animated(filename='examples/example-barv-chart.gif',orientation='v')


Line Charts
With as many lines as data columns in the DataFrame.
import pandas_alive

covid_df = pandas_alive.load_dataset()

covid_df.diff().fillna(0).plot_animated(filename='examples/example-line-chart.gif',kind='line',period_label={'x':0.25,'y':0.9})


Bar Charts
Similar to line charts with time as the x-axis.
import pandas_alive

covid_df = pandas_alive.load_dataset()

covid_df.sum(axis=1).fillna(0).plot_animated(filename='examples/example-bar-chart.gif',kind='bar',
period_label={'x':0.1,'y':0.9},
enable_progress_bar=True, steps_per_period=2, interpolate_period=True, period_length=200
)


Scatter Charts
import pandas as pd
import pandas_alive

max_temp_df = pd.read_csv(
"data/Newcastle_Australia_Max_Temps.csv",
parse_dates={"Timestamp": ["Year", "Month", "Day"]},
)
min_temp_df = pd.read_csv(
"data/Newcastle_Australia_Min_Temps.csv",
parse_dates={"Timestamp": ["Year", "Month", "Day"]},
)

merged_temp_df = pd.merge_asof(max_temp_df, min_temp_df, on="Timestamp")

merged_temp_df.index = pd.to_datetime(merged_temp_df["Timestamp"].dt.strftime('%Y/%m/%d'))

keep_columns = ["Minimum temperature (Degree C)", "Maximum temperature (Degree C)"]

merged_temp_df[keep_columns].resample("Y").mean().plot_animated(filename='examples/example-scatter-chart.gif',kind="scatter",title='Max & Min Temperature Newcastle, Australia')


Pie Charts
import pandas_alive

covid_df = pandas_alive.load_dataset()

covid_df.plot_animated(filename='examples/example-pie-chart.gif',kind="pie",rotatelabels=True,period_label={'x':0,'y':0})


Bubble Charts
Bubble charts are generated from a multi-indexed dataframes. Where the index is the time period (optional) and the axes are defined with x_data_label & y_data_label which should be passed a string in the level 0 column labels.
See an example multi-indexed dataframe at: https://github.com/JackMcKew/pandas_alive/tree/master/data/multi.csv
When you set color_data_label= to a df column name, pandas_alive will automatically add a colorbar.
import pandas_alive

multi_index_df = pd.read_csv("data/multi.csv", header=[0, 1], index_col=0)

multi_index_df.index = pd.to_datetime(multi_index_df.index,dayfirst=True)

map_chart = multi_index_df.plot_animated(
kind="bubble",
filename="examples/example-bubble-chart.gif",
x_data_label="Longitude",
y_data_label="Latitude",
size_data_label="Cases",
color_data_label="Cases",
vmax=5, steps_per_period=3, interpolate_period=True, period_length=500,
dpi=100
)

Bubble Chart Example 1

Bubble Chart Example 2
Jupyter notebook: pendulum_sample.ipynb

GeoSpatial Charts
GeoSpatial charts can now be animated easily using geopandas!

If using Windows, anaconda is the easiest way to install with all GDAL dependancies.

Must begin with a geopandas GeoDataFrame containing 'wide' data where:

Every row represents a single geometry (Point or Polygon).

The index contains the geometry label (optional)


Each column represents a single period in time.


These can be easily composed by transposing data compatible with the rest of the charts using df = df.T.

GeoSpatial Point Charts
import geopandas
import pandas_alive
import contextily

gdf = geopandas.read_file('data/nsw-covid19-cases-by-postcode.gpkg')
gdf.index = gdf.postcode
gdf = gdf.drop('postcode',axis=1)

map_chart = gdf.plot_animated(filename='examples/example-geo-point-chart.gif',basemap_format={'source':contextily.providers.Stamen.Terrain})


Polygon GeoSpatial Charts
Supports GeoDataFrames containing Polygons!
import geopandas
import pandas_alive
import contextily

gdf = geopandas.read_file('data/italy-covid-region.gpkg')
gdf.index = gdf.region
gdf = gdf.drop('region',axis=1)

map_chart = gdf.plot_animated(filename='examples/example-geo-polygon-chart.gif',basemap_format={'source':contextily.providers.Stamen.Terrain})


Multiple Charts
pandas_alive supports multiple animated charts in a single visualisation.

Create a list of all charts to include in animation
Use animate_multiple_plots with a filename and the list of charts (this will use matplotlib.subplots)
Done!

import pandas_alive

covid_df = pandas_alive.load_dataset()

animated_line_chart = covid_df.diff().fillna(0).plot_animated(kind='line',period_label=False,add_legend=False)

animated_bar_chart = covid_df.plot_animated(n_visible=10)

pandas_alive.animate_multiple_plots('examples/example-bar-and-line-chart.gif',[animated_bar_chart,animated_line_chart],
enable_progress_bar=True)


Urban Population
import pandas_alive

urban_df = pandas_alive.load_dataset("urban_pop")

animated_line_chart = (
urban_df.sum(axis=1)
.pct_change()
.fillna(method='bfill')
.mul(100)
.plot_animated(kind="line", title="Total % Change in Population",period_label=False,add_legend=False)
)

animated_bar_chart = urban_df.plot_animated(n_visible=10,title='Top 10 Populous Countries',period_fmt="%Y")

pandas_alive.animate_multiple_plots('examples/example-bar-and-line-urban-chart.gif',[animated_bar_chart,animated_line_chart],
title='Urban Population 1977 - 2018', adjust_subplot_top=0.85, enable_progress_bar=True)


Life Expectancy in G7 Countries
import pandas_alive
import pandas as pd

data_raw = pd.read_csv(
"https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Long%20run%20life%20expectancy%20-%20Gapminder%2C%20UN/Long%20run%20life%20expectancy%20-%20Gapminder%2C%20UN.csv"
)

list_G7 = [
"Canada",
"France",
"Germany",
"Italy",
"Japan",
"United Kingdom",
"United States",
]

data_raw = data_raw.pivot(
index="Year", columns="Entity", values="Life expectancy (Gapminder, UN)"
)

data = pd.DataFrame()
data["Year"] = data_raw.reset_index()["Year"]
for country in list_G7:
data[country] = data_raw[country].values

data = data.fillna(method="pad")
data = data.fillna(0)
data = data.set_index("Year").loc[1900:].reset_index()

data["Year"] = pd.to_datetime(data.reset_index()["Year"].astype(str))

data = data.set_index("Year")

animated_bar_chart = data.plot_animated(
period_fmt="%Y",perpendicular_bar_func="mean", period_length=200,fixed_max=True
)

animated_line_chart = data.plot_animated(
kind="line", period_fmt="%Y", period_length=200,fixed_max=True
)

pandas_alive.animate_multiple_plots(
"examples/life-expectancy.gif",
plots=[animated_bar_chart, animated_line_chart],
title="Life expectancy in G7 countries up to 2015",
adjust_subplot_left=0.2, adjust_subplot_top=0.9, enable_progress_bar=True
)


NSW COVID Visualisation
import geopandas
import pandas as pd
import pandas_alive
import contextily
import matplotlib.pyplot as plt

import urllib.request, json

with urllib.request.urlopen(
"https://data.nsw.gov.au/data/api/3/action/package_show?id=aefcde60-3b0c-4bc0-9af1-6fe652944ec2"
) as url:
data = json.loads(url.read().decode())

# Extract url to csv component
covid_nsw_data_url = data["result"]["resources"][0]["url"]

# Read csv from data API url
nsw_covid = pd.read_csv(covid_nsw_data_url)
postcode_dataset = pd.read_csv("data/postcode-data.csv")

# Prepare data from NSW health dataset


nsw_covid = nsw_covid.fillna(9999)
nsw_covid["postcode"] = nsw_covid["postcode"].astype(int)

grouped_df = nsw_covid.groupby(["notification_date", "postcode"]).size()
grouped_df = pd.DataFrame(grouped_df).unstack()
grouped_df.columns = grouped_df.columns.droplevel().astype(str)

grouped_df = grouped_df.fillna(0)
grouped_df.index = pd.to_datetime(grouped_df.index)

cases_df = grouped_df

# Clean data in postcode dataset prior to matching

grouped_df = grouped_df.T
postcode_dataset = postcode_dataset[postcode_dataset['Longitude'].notna()]
postcode_dataset = postcode_dataset[postcode_dataset['Longitude'] != 0]
postcode_dataset = postcode_dataset[postcode_dataset['Latitude'].notna()]
postcode_dataset = postcode_dataset[postcode_dataset['Latitude'] != 0]
postcode_dataset['Postcode'] = postcode_dataset['Postcode'].astype(str)

# Build GeoDataFrame from Lat Long dataset and make map chart
grouped_df['Longitude'] = grouped_df.index.map(postcode_dataset.set_index('Postcode')['Longitude'].to_dict())
grouped_df['Latitude'] = grouped_df.index.map(postcode_dataset.set_index('Postcode')['Latitude'].to_dict())
gdf = geopandas.GeoDataFrame(
grouped_df, geometry=geopandas.points_from_xy(grouped_df.Longitude, grouped_df.Latitude),crs="EPSG:4326")
gdf = gdf.dropna()

# Prepare GeoDataFrame for writing to geopackage
gdf = gdf.drop(['Longitude','Latitude'],axis=1)
gdf.columns = gdf.columns.astype(str)
gdf['postcode'] = gdf.index
gdf.to_file("data/nsw-covid19-cases-by-postcode.gpkg", layer='nsw-postcode-covid', driver="GPKG")

# Prepare GeoDataFrame for plotting
gdf.index = gdf.postcode
gdf = gdf.drop('postcode',axis=1)
gdf = gdf.to_crs("EPSG:3857") #Web Mercator

map_chart = gdf.plot_animated(basemap_format={'source':contextily.providers.Stamen.Terrain},cmap='cool')

cases_df.to_csv('data/nsw-covid-cases-by-postcode.csv')

from datetime import datetime

bar_chart = cases_df.sum(axis=1).plot_animated(
kind='line',
label_events={
'Ruby Princess Disembark':datetime.strptime("19/03/2020", "%d/%m/%Y"),
'Lockdown':datetime.strptime("31/03/2020", "%d/%m/%Y")
},
fill_under_line_color="blue",
add_legend=False
)

map_chart.ax.set_title('Cases by Location')

grouped_df = pd.read_csv('data/nsw-covid-cases-by-postcode.csv', index_col=0, parse_dates=[0])

line_chart = (
grouped_df.sum(axis=1)
.cumsum()
.fillna(0)
.plot_animated(kind="line", period_label=False, title="Cumulative Total Cases", add_legend=False)
)


def current_total(values):
total = values.sum()
s = f'Total : {int(total)}'
return {'x': .85, 'y': .2, 's': s, 'ha': 'right', 'size': 11}

race_chart = grouped_df.cumsum().plot_animated(
n_visible=5, title="Cases by Postcode", period_label=False,period_summary_func=current_total
)

import time

timestr = time.strftime("%d/%m/%Y")

plots = [bar_chart, line_chart, map_chart, race_chart]

from matplotlib import rcParams

rcParams.update({"figure.autolayout": False})
# make sure figures are `Figure()` instances
figs = plt.Figure()
gs = figs.add_gridspec(2, 3, hspace=0.5)
f3_ax1 = figs.add_subplot(gs[0, :])
f3_ax1.set_title(bar_chart.title)
bar_chart.ax = f3_ax1

f3_ax2 = figs.add_subplot(gs[1, 0])
f3_ax2.set_title(line_chart.title)
line_chart.ax = f3_ax2

f3_ax3 = figs.add_subplot(gs[1, 1])
f3_ax3.set_title(map_chart.title)
map_chart.ax = f3_ax3

f3_ax4 = figs.add_subplot(gs[1, 2])
f3_ax4.set_title(race_chart.title)
race_chart.ax = f3_ax4

timestr = cases_df.index.max().strftime("%d/%m/%Y")
figs.suptitle(f"NSW COVID-19 Confirmed Cases up to {timestr}")

pandas_alive.animate_multiple_plots(
'examples/nsw-covid.gif',
plots,
figs,
enable_progress_bar=True
)


Italy COVID Visualisation
import geopandas
import pandas as pd
import pandas_alive
import contextily
import matplotlib.pyplot as plt


region_gdf = geopandas.read_file('data\geo-data\italy-with-regions')
region_gdf.NOME_REG = region_gdf.NOME_REG.str.lower().str.title()
region_gdf = region_gdf.replace('Trentino-Alto Adige/Sudtirol','Trentino-Alto Adige')
region_gdf = region_gdf.replace("Valle D'Aosta/Vallée D'Aoste\r\nValle D'Aosta/Vallée D'Aoste","Valle d'Aosta")

italy_df = pd.read_csv('data\Regional Data - Sheet1.csv',index_col=0,header=1,parse_dates=[0])

italy_df = italy_df[italy_df['Region'] != 'NA']

cases_df = italy_df.iloc[:,:3]
cases_df['Date'] = cases_df.index
pivoted = cases_df.pivot(values='New positives',index='Date',columns='Region')
pivoted.columns = pivoted.columns.astype(str)
pivoted = pivoted.rename(columns={'nan':'Unknown Region'})

cases_gdf = pivoted.T
cases_gdf['geometry'] = cases_gdf.index.map(region_gdf.set_index('NOME_REG')['geometry'].to_dict())

cases_gdf = cases_gdf[cases_gdf['geometry'].notna()]

cases_gdf = geopandas.GeoDataFrame(cases_gdf, crs=region_gdf.crs, geometry=cases_gdf.geometry)

gdf = cases_gdf

map_chart = gdf.plot_animated(basemap_format={'source':contextily.providers.Stamen.Terrain},cmap='viridis')

cases_df = pivoted

from datetime import datetime

bar_chart = cases_df.sum(axis=1).plot_animated(
kind='line',
label_events={
'Schools Close':datetime.strptime("4/03/2020", "%d/%m/%Y"),
'Phase I Lockdown':datetime.strptime("11/03/2020", "%d/%m/%Y"),
'1M Global Cases':datetime.strptime("02/04/2020", "%d/%m/%Y"),
'100k Global Deaths':datetime.strptime("10/04/2020", "%d/%m/%Y"),
'Manufacturing Reopens':datetime.strptime("26/04/2020", "%d/%m/%Y"),
'Phase II Lockdown':datetime.strptime("4/05/2020", "%d/%m/%Y"),

},
fill_under_line_color="blue",
add_legend=False
)

map_chart.ax.set_title('Cases by Location')

line_chart = (
cases_df.sum(axis=1)
.cumsum()
.fillna(0)
.plot_animated(kind="line", period_label=False, title="Cumulative Total Cases",add_legend=False)
)


def current_total(values):
total = values.sum()
s = f'Total : {int(total)}'
return {'x': .85, 'y': .1, 's': s, 'ha': 'right', 'size': 11}

race_chart = cases_df.cumsum().plot_animated(
n_visible=5, title="Cases by Region", period_label=False,period_summary_func=current_total
)

import time

timestr = time.strftime("%d/%m/%Y")

plots = [bar_chart, race_chart, map_chart, line_chart]

# Otherwise titles overlap and adjust_subplot does nothing
from matplotlib import rcParams
from matplotlib.animation import FuncAnimation

rcParams.update({"figure.autolayout": False})
# make sure figures are `Figure()` instances
figs = plt.Figure()
gs = figs.add_gridspec(2, 3, hspace=0.5)
f3_ax1 = figs.add_subplot(gs[0, :])
f3_ax1.set_title(bar_chart.title)
bar_chart.ax = f3_ax1

f3_ax2 = figs.add_subplot(gs[1, 0])
f3_ax2.set_title(race_chart.title)
race_chart.ax = f3_ax2

f3_ax3 = figs.add_subplot(gs[1, 1])
f3_ax3.set_title(map_chart.title)
map_chart.ax = f3_ax3

f3_ax4 = figs.add_subplot(gs[1, 2])
f3_ax4.set_title(line_chart.title)
line_chart.ax = f3_ax4

axes = [f3_ax1, f3_ax2, f3_ax3, f3_ax4]
timestr = cases_df.index.max().strftime("%d/%m/%Y")
figs.suptitle(f"Italy COVID-19 Confirmed Cases up to {timestr}")

pandas_alive.animate_multiple_plots(
'examples/italy-covid.gif',
plots,
figs,
enable_progress_bar=True
)


Simple Pendulum Motion
Jupyter notebook: pendulum_sample.ipynb

HTML 5 Videos
Pandas_Alive supports rendering HTML5 videos through the use of df.plot_animated().get_html5_video(). .get_html5_video saves the animation as an h264 video, encoded in base64 directly into the HTML5 video tag. This respects the rc parameters for the writer as well as the bitrate. This also makes use of the interval to control the speed, and uses the repeat parameter to decide whether to loop.
This is typically used in Jupyter notebooks.
import pandas_alive
from IPython.display import HTML

covid_df = pandas_alive.load_dataset()

animated_html = covid_df.plot_animated().get_html5_video()

HTML(animated_html)

Progress Bars!
Generating animations can take some time, so enable progress bars by installing tqdm with pip install tqdm or conda install tqdm and using the keyword enable_progress_bar=True together with filename=movie file name.
By default Pandas_Alive will create a tqdm progress bar when saving to a file, for the number of frames to animate, and update the progres bar after each frame.
import pandas_alive

covid_df = pandas_alive.load_dataset()
# add a filename=movie.mp4 or movie.gif to save to, in order to see the progress bar in action
covid_df.plot_animated(enable_progress_bar=True)

Example of TQDM in action:

Future Features
A list of future features that may/may not be developed is:

Add to line & scatter charts the ability to plot 'X' vs 'Y', as already implemented with bubble plots.
Add option of a colorbar for bubble plots when included in multiple plots. Currently only available for single bubble chart animations.
Geographic charts (currently using OSM export image, potential geopandas)
Loading bar support (potential tqdm or alive-progress)
Potentially support writing to GIF in memory with https://github.com/maxhumber/gif
Support custom figures & axes for multiple plots (eg, gridspec)

Tutorials
Find tutorials on how to use Pandas_Alive over at:

https://jackmckew.dev/creating-animated-plots-with-pandas_alive.html
https://jackmckew.dev/geopandas-and-pandas-alive.html
Jupyter notebooks in test_notebooks folder.

Inspiration
The inspiration for this project comes from:

bar_chart_race by Ted Petrou
Pandas-Bokeh by Patrik Hlobil

Requirements
If you get an error such as TypeError: 'MovieWriterRegistry' object is not an iterator, this signals there isn't a writer library installed on your machine.
This package utilises the matplotlib.animation function, thus requiring a writer library.
Ensure to have one of the supported tooling software installed prior to use!

ffmpeg
ImageMagick
See more at https://matplotlib.org/3.2.1/api/animation_api.html#writer-classes


If the output file name has an extension of .gif, pandas_alive will write this with PIL in memory.

Documentation
Documentation is provided at https://jackmckew.github.io/pandas_alive/
Contributing
Pull requests are welcome! Please help to cover more and more chart types!
Development
To get started in development, clone a copy of this repository to your PC. This will now enable you to create a Jupyter notebook or a standalone .py file, and import pandas_alive as a local module. Now you can create new chart types in pandas_alive/charts.py or pandas_alive/geocharts.py to build to your hearts content!
For Python packages for a development environment check requirements.txt if using PIP, or py38-pandas_alive.yml if using conda.
If you are using conda and are new to setting up environments for collaboration on projects, here are some notes from a previous contributor using conda: Python set up with conda for project collaboration
If you wish to contribute new Jupyter notebooks with different application examples, please place them in this directory: ./examples/test_notebooks/.
Changelog