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()
Install with pip install pandas_alive
As this package builds upon 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 usedf.plot_animated().get_html5_videoto return a HTML5 video
- Either specify a file name to write to with
- Done!
import pandas_alive
covid_df = pandas_alive.load_dataset()
covid_df.plot_animated(filename='examples/example-barh-chart.gif')pandas_alive current supports:
import pandas_alive
covid_df = pandas_alive.load_dataset()
covid_df.plot_animated(filename='example-barh-chart.gif')
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()
covid_df.plot_animated(filename='examples/example-barv-chart.gif',orientation='v')
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':0.1,'y':0.9})
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')
pandas_alive supports multiple animated charts in a single visualisation.
- Create a list of all charts to include in animation
- Use
animate_multiple_plotswith afilenameand the list of charts (this will usematplotlib.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)
animated_bar_chart = covid_df.plot_animated(kind='barh',n_visible=10)
pandas_alive.animate_multiple_plots('examples/example-bar-and-line-chart.gif',[animated_bar_chart,animated_line_chart])
import pandas_alive
urban_df = pandas_alive.load_dataset("urban_pop")
animated_line_chart = (
urban_df.sum(axis=1)
.pct_change()
.dropna()
.mul(100)
.plot_animated(kind="line", title="Total % Change in Population",period_label=False)
)
animated_bar_chart = urban_df.plot_animated(kind='barh',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)
The inspiration for this project comes from:
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
- Pillow
- See more at https://matplotlib.org/3.2.1/api/animation_api.html#writer-classes
Documentation is provided at https://jackmckew.github.io/pandas_alive/
Pull requests are welcome! Please help to cover more and more chart types!