Python可视化图库绘制动态图表源码

import pandas_alive import pandas as pd import matplotlib.pyplot as plt # 中文显示 plt.rcParams['font.sans-serif'] = ['SimHei'] # Windows plt.rcParams['font.sans-serif'] = ['Hiragino Sans GB'] # Mac plt.rcParams['axes.unicode_minus'] = False # 读取数据 df_result = pd.read_csv('data/yuhuanshui.csv', index_col=0, parse_dates=[0]) # 生成图表 animated_line_chart = df_result.diff().fillna(0).plot_animated(kind='line', period_label=False, add_legend=False) animated_bar_chart = df_result.plot_animated(n_visible=10) pandas_alive.animate_multiple_plots('examples/yuhuanshui.gif', [animated_bar_chart, animated_line_chart], enable_progress_bar=True, title='我是余欢水演职人员热度排行') # covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0]) # covid_df.plot_animated(filename='examples/example-barh-chart.gif', n_visible=15) # elec_df = pd.read_csv("data/Aus_Elec_Gen_1980_2018.csv", index_col=0, parse_dates=[0], thousands=',') # elec_df = elec_df.iloc[:20, :] # elec_df.fillna(0).plot_animated('examples/example-electricity-generated-australia.gif', period_fmt="%Y", # title='Australian Electricity Generation Sources 1980-2018') # covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0]) # covid_df.diff().fillna(0).plot_animated(filename='examples/example-line-chart.gif', kind='line', period_label={'x': 0.25, 'y': 0.9}) # covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0]) # 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 # ) def scatter_chart(): 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"]}, ) max_temp_df = max_temp_df.iloc[:5000, :] min_temp_df = min_temp_df.iloc[:5000, :] 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') def pie_chart(): covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0]) covid_df.plot_animated(filename='examples/example-pie-chart.gif', kind="pie", rotatelabels=True, period_label={'x': 0, 'y': 0}) def buddle_chart(): 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 ) def geo_point_chart(): 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) result = gdf.iloc[:, :20] result['geometry'] = gdf.iloc[:, -1:]['geometry'] map_chart = result.plot_animated(filename='examples/example-geo-point-chart.gif', basemap_format={'source': contextily.providers.Stamen.Terrain}) def geo_polygon_chart(): 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) result = gdf.iloc[:, :20] result['geometry'] = gdf.iloc[:, -1:]['geometry'] map_chart = result.plot_animated(filename='examples/example-geo-polygon-chart.gif', basemap_format={'source': contextily.providers.Stamen.Terrain}) def bar_and_line_chart(): covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0]) 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) def population(): urban_df = pd.read_csv("data/urban_pop.csv", index_col=0, parse_dates=[0]) 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) # population() def life(): data_raw = pd.read_csv("data/long.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") data = data.iloc[:25, :] 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 ) # life() def nsw(): import geopandas import pandas as pd import pandas_alive import contextily import matplotlib.pyplot as plt import json with open('data/package_show.json', 'r', encoding='utf8')as fp: data = json.load(fp) # Extract url to csv component covid_nsw_data_url = data["result"]["resources"][0]["url"] print(covid_nsw_data_url) # Read csv from data API url nsw_covid = pd.read_csv('data/confirmed_cases_table1_location.csv') 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", "