pandas alive

pandas-alive.ipynb

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      "source": "import pandas_alive",
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      "metadata": {},
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      "source": "Download sample data from https://github.com/JackMcKew/pandas_alive/blob/main/data/covid19.csv"
    },
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      "cell_type": "code",
      "source": "covid_df = pandas_alive.load_dataset()",
      "execution_count": 2,
      "outputs": []
    },
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      "source": "covid_df.plot_animated(filename='covid.mp4')",
      "execution_count": 3,
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          "text": "Generating BarChartRace, plotting ['Belgium', 'Brazil', 'Canada', 'China', 'France', 'Germany', 'India', 'Indonesia', 'Iran', 'Ireland', 'Italy', 'Mexico', 'Netherlands', 'Portugal', 'Spain', 'Sweden', 'Switzerland', 'Turkey', 'USA', 'United Kingdom']\n",
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          "text": "/home/qiusheng/.conda/envs/geemap/lib/python3.8/site-packages/pandas_alive/charts.py:221: UserWarning: FixedFormatter should only be used together with FixedLocator\n  ax.set_yticklabels(self.df.columns)\n/home/qiusheng/.conda/envs/geemap/lib/python3.8/site-packages/pandas_alive/charts.py:222: UserWarning: FixedFormatter should only be used together with FixedLocator\n  ax.set_xticklabels([max_val] * len(ax.get_xticks()))\n/home/qiusheng/.conda/envs/geemap/lib/python3.8/site-packages/pandas_alive/charts.py:68: UserWarning: Plotting too many bars may result in undesirable output, use `n_visible=5 to limit number of bars\n  warnings.warn(\n",
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            "text/plain": "BarChartRace(df=                     Belgium  Brazil  Canada   China   France  Germany  India  \\\ndate                                                                            \n2020-02-26 00:00:00      NaN     NaN     NaN  2717.0      2.0      NaN    NaN   \n2020-02-26 02:24:00      NaN     NaN     NaN  2719.9      2.0      NaN    NaN   \n2020-02-26 04:48:00      NaN     NaN     NaN  2722.8      2.0      NaN    NaN   \n2020-02-26 07:12:00      NaN     NaN     NaN  2725.7      2.0      NaN    NaN   \n2020-02-26 09:36:00      NaN     NaN     NaN  2728.6      2.0      NaN    NaN   \n...                      ...     ...     ...     ...      ...      ...    ...   \n2020-04-21 14:24:00   6156.4  2840.0  2008.2  4636.0  21155.4   5180.6  666.6   \n2020-04-21 16:48:00   6182.8  2856.5  2024.9  4636.0  21209.8   5205.2  670.2   \n2020-04-21 19:12:00   6209.2  2873.0  2041.6  4636.0  21264.2   5229.8  673.8   \n2020-04-21 21:36:00   6235.6  2889.5  2058.3  4636.0  21318.6   5254.4  677.4   \n2020-04-22 00:00:00   6262.0  2906.0  2075.0  4636.0  21373.0   5279.0  681.0   \n\n                     Indonesia    Iran  Ireland    Italy  Mexico  Netherlands  \\\ndate                                                                            \n2020-02-26 00:00:00        NaN    19.0      NaN     12.0     NaN          NaN   \n2020-02-26 02:24:00        NaN    19.7      NaN     12.5     NaN          NaN   \n2020-02-26 04:48:00        NaN    20.4      NaN     13.0     NaN          NaN   \n2020-02-26 07:12:00        NaN    21.1      NaN     13.5     NaN          NaN   \n2020-02-26 09:36:00        NaN    21.8      NaN     14.0     NaN          NaN   \n...                        ...     ...      ...      ...     ...          ...   \n2020-04-21 14:24:00      627.4  5353.4    753.4  24910.2   799.0       4012.4   \n2020-04-21 16:48:00      629.3  5362.8    757.3  24953.9   813.5       4026.3   \n2020-04-21 19:12:00      631.2  5372.2    761.2  24997.6   828.0       4040.2   \n2020-04-21 21:36:00      633.1  5381.6    765.1  25041.3   842.5       4054.1   \n2020-04-22 00:00:00      635.0  5391.0    769.0  25085.0   857.0       4068.0   \n\n                     Portugal    Spain  Sweden  Switzerland  Turkey      USA  \\\ndate                                                                           \n2020-02-26 00:00:00       NaN      NaN     NaN          NaN     NaN      NaN   \n2020-02-26 02:24:00       NaN      NaN     NaN          NaN     NaN      NaN   \n2020-02-26 04:48:00       NaN      NaN     NaN          NaN     NaN      NaN   \n2020-02-26 07:12:00       NaN      NaN     NaN          NaN     NaN      NaN   \n2020-02-26 09:36:00       NaN      NaN     NaN          NaN     NaN      NaN   \n...                       ...      ...     ...          ...     ...      ...   \n2020-04-21 14:24:00     775.8  21543.0  1868.2       1496.6  2329.2  45755.6   \n2020-04-21 16:48:00     778.1  21586.5  1885.4       1499.7  2340.9  45973.7   \n2020-04-21 19:12:00     780.4  21630.0  1902.6       1502.8  2352.6  46191.8   \n2020-04-21 21:36:00     782.7  21673.5  1919.8       1505.9  2364.3  46409.9   \n2020-04-22 00:00:00     785.0  21717.0  1937.0       1509.0  2376.0  46628.0   \n\n                     United Kingdom  \ndate                                 \n2020-02-26 00:00:00             NaN  \n2020-02-26 02:24:00             NaN  \n2020-02-26 04:48:00             NaN  \n2020-02-26 07:12:00             NaN  \n2020-02-26 09:36:00             NaN  \n...                             ...  \n2020-04-21 14:24:00         17841.8  \n2020-04-21 16:48:00         17919.1  \n2020-04-21 19:12:00         17996.4  \n2020-04-21 21:36:00         18073.7  \n2020-04-22 00:00:00         18151.0  \n\n[561 rows x 20 columns], interpolate_period=True, steps_per_period=10, period_length=500, period_fmt='%d/%m/%Y', figsize=array([7.18229167, 3.5       ]), title=None, fig=<Figure size 1034x504 with 1 Axes>, cmap='dark24', tick_label_size=7, period_label=True, period_summary_func=None, fixed_max=False, dpi=144, writer=None, enable_progress_bar=False, kwargs={}, orientation='h', sort='desc', label_bars=True, bar_label_size=7, n_visible=20, fixed_order=False, perpendicular_bar_func=None)"
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