python能否做动态折线图 python绘制动态曲线

关于动态条形图，小F以前推荐过「Bar Chart Race」这个库。三行代码就能实现动态条形图的绘制。

有些同学在使用的时候，会出现一些错误。一个是加载文件报错，另一个是生成GIF的时候报错。

这是因为作者的示例是网络加载数据，会读取不到。通过读取本地文件，就不会出错。

GIF生成失败一般是需要安装imagemagick(图片处理工具)。

最近小F又发现一个可视化图库「Pandas_Alive」，不仅包含动态条形图，还可以绘制动态曲线图、气泡图、饼状图、地图等。

同样也是几行代码就能完成动态图表的绘制。

GitHub地址：

https://github.com/JackMcKew/pandas_alive

使用文档：https://jackmckew.github.io/pandas_alive/

安装版本建议是0.2.3，matplotlib版本是3.2.1。

同时需自行安装tqdm(显示进度条)和descartes(绘制地图相关库)。

要不然会出现报错，估计是作者的requestment.txt没包含这两个库。

好了，成功安装后就可以引入这个第三方库，直接选择加载本地文件。

import pandas_alive
import pandas as pd
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)

生成了一个GIF图，具体如下。

刚开始学习这个库的时候，大家可以减少数据，这样生成GIF的时间就会快一些。

比如小F在接下来的实践中，基本都只选取了20天左右的数据。

对于其他图表，我们可以查看官方文档的API说明，得以了解。

下面我们就来看看其他动态图表的绘制方法吧！

01 动态条形图

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')

02 动态柱状图

covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])
covid_df.plot_animated(filename='examples/example-barv-chart.gif', orientation='v', n_visible=15)

03 动态曲线图

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})

04 动态面积图

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
)

05 动态散点图

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')

06 动态饼状图

07 动态气泡图

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})

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
)

08 地理空间点图表

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})

09 多边形地理图表

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})

10 多个动态图表

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)

11 城市人口

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)

12 G7国家平均寿命

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
)

13 新南威尔斯州COVID可视化

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", "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
result = gdf.iloc[:, :22]
result['geometry'] = gdf.iloc[:, -1:]['geometry']
gdf = result
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')
cases_df = cases_df.iloc[:22, :]
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])
grouped_df = grouped_df.iloc[:22, :]
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
)

14 意大利COVID可视化

def italy():

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
result = gdf.iloc[:, :22]
result['geometry'] = gdf.iloc[:, -1:]['geometry']
gdf = result
map_chart = gdf.plot_animated(basemap_format={'source': contextily.providers.Stamen.Terrain}, cmap='viridis')
cases_df = pivoted
cases_df = cases_df.iloc[:22, :]
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
)

最后如果你想完成中文动态图表的制作，加入中文显示代码即可。

# 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')

还是使用演员的百度指数数据。