pandas_alive-0.1.14.tar.gz

# Pandas_Alive Animated plotting extension for Pandas with Matplotlib [](https://jackmckew.github.io/pandas_alive/) [](https://pypi.python.org/pypi/pandas_alive/) [](https://pypi.python.org/pypi/pandas_alive/) [](https://pypi.python.org/pypi/pandas_alive/) [](https://www.buymeacoffee.com/jackmckew) **Pandas_Alive** is intended to provide a plotting backend for animated [matplotlib](https://matplotlib.org/) charts for [Pandas](https://pandas.pydata.org/) DataFrames, similar to the already [existing Visualization feature of Pandas](https://pandas.pydata.org/pandas-docs/stable/visualization.html). With **Pandas_Alive**, creating stunning, animated visualisations is as easy as calling: `df.plot_animated()`  ## Table of Contents <!-- START doctoc --> <!-- END doctoc --> ## Installation Install with `pip install pandas_alive` ## Usage As this package builds upon [`bar_chart_race`](https://github.com/dexplo/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](#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! ``` python 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 ``` python 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') ```  ``` python 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) ```  ``` python 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) ```  ``` python 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 ``` python 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. ``` python 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}) ```  #### Bar Charts Similar to line charts with time as the x-axis ``` python 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}) ```  #### Scatter Charts ``` python 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 ``` python 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> ``` python 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", ) ```  ### 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! ``` python 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(n_visible=10) pandas_alive.animate_multiple_plots('examples/example-bar-and-line-chart.gif',[animated_bar_chart,animated_line_chart]) ```  #### Urban Population ``` python 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(n_visible=10,title='Top 10 Populous Countries',period_fmt="%Y") pandas_alive.animate_m