python 动态图表 大屏_爱了！Python 动态图表太太太秀了！
原標題：愛了！Python 動態圖表太太太秀了！
本文轉自：法納斯特，作者：小F
關于動態條形圖，小F以前推薦過 「Bar Chart Race」這個庫。三行代碼就能實現動態條形圖的繪制。
有些同學在使用的時候，會出現一些錯誤。一個是加載文件報錯，另一個是生成GIF的時候報錯。
這是因為作者的示例是網絡加載數據，會讀取不到。通過讀取本地文件，就不會出錯。
GIF生成失敗一般是需要安裝 imagemagick(圖片處理工具)。
最近小F又發現一 個可視化圖庫「 Pandas_Alive」，不僅包含動態條形圖，還 可以繪制動態曲線圖、氣泡圖、餅狀圖、地圖等。
同樣也是幾行代碼就能完成動態圖表的繪制。
GitHub地址：
https://github.com/JackMcKew/pandas_alive
使用文檔：https://jackmckew.github.io/pandas_alive/
安裝版本建議是0.2.3， matplotlib版本是3.2.1。
同時需自行安裝tqdm(顯示進度條)和descartes(繪制地圖相關庫)。
要不然會出現報錯，估計是作者的requestment.txt沒包含這兩個庫。
好了，成功 安裝后就可以引入這個第三方庫，直接選擇加載本地文件。
importpandas_alive
importpandas aspd
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)
生成了一個GIF圖，具體如下。
剛開始學習這個庫的時候，大家可以 減少數據，這樣 生成GIF的時間就會快一些。
比如小F在接下來的實踐中，基本都只選取了20天左右的數據。
對于其他圖表，我們可以查看官方文檔的API說明，得以了解。
下面我們就來看看其他動態圖表的繪制方法吧！
01 動態條形圖
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 動態柱狀圖
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 動態曲線圖
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 動態面積圖
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 動態散點圖
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 動態餅狀圖
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})
07 動態氣泡圖
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 地理空間點圖表
importgeopandas
importpandas_alive
importcontextily
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 多邊形地理圖表
importgeopandas
importpandas_alive
importcontextily
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 多個動態圖表
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
defpopulation:
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國家平均壽命
deflife:
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"]
forcountry inlist_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 新南威爾斯州COVID可視化
defnsw:
importgeopandas
importpandas aspd
importpandas_alive
importcontextily
importmatplotlib.pyplot asplt
importjson
withopen( 'data/package_show.json', 'r', encoding= 'utf8') asfp:
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, :]
fromdatetime importdatetime
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)
)
defcurrent_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
)
importtime
timestr = time.strftime( "%d/%m/%Y")
plots = [bar_chart, line_chart, map_chart, race_chart]
frommatplotlib importrcParams
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可視化
defitaly:
importgeopandas
importpandas aspd
importpandas_alive
importcontextily
importmatplotlib.pyplot asplt
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/Vall??e D'AosternValle D'Aosta/Vall??e 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, :]
fromdatetime importdatetime
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)
)
defcurrent_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
)
importtime
timestr = time.strftime( "%d/%m/%Y")
plots = [bar_chart, race_chart, map_chart, line_chart]
# Otherwise titles overlap and adjust_subplot does nothing
frommatplotlib importrcParams
frommatplotlib.animation importFuncAnimation
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
)
15 單擺運動
defsimple:
importpandas aspd
importmatplotlib.pyplot asplt
importpandas_alive
importnumpy asnp
# Physical constants
g = 9.81
L = .4
mu = 0.2
THETA_0 = np.pi * 70/ 180# init angle = 70degs
THETA_DOT_0 = 0# no init angVel
DELTA_T = 0.01# time stepping
T = 1.5# time period
# Definition of ODE (ordinary differential equation)
defget_theta_double_dot(theta, theta_dot):
return-mu * theta_dot - (g / L) * np.sin(theta)
# Solution to the differential equation
defpendulum(t):
# initialise changing values
theta = THETA_0
theta_dot = THETA_DOT_0
delta_t = DELTA_T
ang = []
ang_vel = []
ang_acc = []
times = []
fortime innp.arange( 0, t, delta_t):
theta_double_dot = get_theta_double_dot(
theta, theta_dot
)
theta += theta_dot * delta_t
theta_dot += theta_double_dot * delta_t
times.append(time)
ang.append(theta)
ang_vel.append(theta_dot)
ang_acc.append(theta_double_dot)
data = np.array([ang, ang_vel, ang_acc])
returnpd.DataFrame(data=data.T, index=np.array(times), columns=[ "angle", "ang_vel", "ang_acc"])
# units used for ref: ["angle [rad]", "ang_vel [rad/s]", "ang_acc [rad/s^2]"]
df = pendulum(T)
df.index.names = [ "Time (s)"]
print(df)
# generate dataFrame for animated bubble plot
df2 = pd.DataFrame(index=df.index)
df2[ "dx (m)"] = L * np.sin(df[ "angle"])
df2[ "dy (m)"] = -L * np.cos(df[ "angle"])
df2[ "ang_vel"] = abs(df[ "ang_vel"])
df2[ "size"] = df2[ "ang_vel"] * 100# scale angular vels to get nice size on bubble plot
print(df2)
# static pandas plots
#
# print(plt.style.available)
# NOTE:2 lines below required in Jupyter to switch styles correctly
plt.rcParams.update(plt.rcParamsDefault)
plt.style.use( "ggplot") # set plot style
fig, (ax1a, ax2b) = plt.subplots( 1, 2, figsize=( 8, 4), dpi= 100) # 1 row, 2 subplots
# fig.subplots_adjust(wspace=0.1) # space subplots in row
fig.set_tight_layout( True)
fontsize = "small"
df.plot(ax=ax1a).legend(fontsize=fontsize)
ax1a.set_title( "Outputs vs Time", fontsize= "medium")
ax1a.set_xlabel( 'Time [s]', fontsize=fontsize)
ax1a.set_ylabel( 'Amplitudes', fontsize=fontsize);
df.plot(ax=ax2b, x= "angle", y=[ "ang_vel", "ang_acc"]).legend(fontsize=fontsize)
ax2b.set_title( "Outputs vs Angle | Phase-Space", fontsize= "medium")
ax2b.set_xlabel( 'Angle [rad]', fontsize=fontsize)
ax2b.set_ylabel( 'Angular Velocity / Acc', fontsize=fontsize)
# sample scatter plot with colorbar
fig, ax = plt.subplots
sc = ax.scatter(df2[ "dx (m)"], df2[ "dy (m)"], s=df2[ "size"] * .1, c=df2[ "ang_vel"], cmap= "jet")
cbar = fig.colorbar(sc)
cbar.set_label(label= "ang_vel [rad/s]", fontsize= "small")
# sc.set_clim(350, 400)
ax.tick_params(labelrotation= 0, labelsize= "medium")
ax_scale = 1.
ax.set_xlim(-L * ax_scale, L * ax_scale)
ax.set_ylim(-L * ax_scale - 0.1, L * ax_scale - 0.1)
# make axes square: a circle shows as a circle
ax.set_aspect( 1/ ax.get_data_ratio)
ax.arrow( 0, 0, df2[ "dx (m)"].iloc[ -1], df2[ "dy (m)"].iloc[ -1],
color= "dimgray", ls= ":", lw= 2.5, width= .0, head_width= 0, zorder= -1
)
ax.text( 0, 0.15, s= "size and colour of pendulum bobnbased on pd columnnfor angular velocity",
ha= 'center', va= 'center')
# plt.show
dpi = 100
ax_scale = 1.1
figsize = ( 3, 3)
fontsize = "small"
# set up figure to pass onto `pandas_alive`
# NOTE:by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast!
# fig1, ax1 = plt.subplots
fig1 = plt.Figure
ax1 = fig1.add_subplot
fig1.set_size_inches(figsize)
ax1.set_title( "Simple pendulum animation, L="+ str(L) + "m", fontsize= "medium")
ax1.set_xlabel( "Time (s)", color= 'dimgray', fontsize=fontsize)
ax1.set_ylabel( "Amplitudes", color= 'dimgray', fontsize=fontsize)
ax1.tick_params(labelsize=fontsize)
# pandas_alive
line_chart = df.plot_animated(filename= "pend-line.gif", kind= 'line', period_label={ 'x': 0.05, 'y': 0.9},
steps_per_period= 1, interpolate_period= False, period_length= 50,
period_fmt= 'Time:{x:10.2f}',
enable_progress_bar= True, fixed_max= True, dpi= 100, fig=fig1
)
plt.close
# Video('examples/pend-line.mp4', html_attributes="controls muted autoplay")
# set up and generate animated scatter plot
#
# set up figure to pass onto `pandas_alive`
# NOTE:by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast!
fig1sc = plt.Figure
ax1sc = fig1sc.add_subplot
fig1sc.set_size_inches(figsize)
ax1sc.set_title( "Simple pendulum animation, L="+ str(L) + "m", fontsize= "medium")
ax1sc.set_xlabel( "Time (s)", color= 'dimgray', fontsize=fontsize)
ax1sc.set_ylabel( "Amplitudes", color= 'dimgray', fontsize=fontsize)
ax1sc.tick_params(labelsize=fontsize)
# pandas_alive
scatter_chart = df.plot_animated(filename= "pend-scatter.gif", kind= 'scatter', period_label={ 'x': 0.05, 'y': 0.9},
steps_per_period= 1, interpolate_period= False, period_length= 50,
period_fmt= 'Time:{x:10.2f}',
enable_progress_bar= True, fixed_max= True, dpi= 100, fig=fig1sc, size= "ang_vel"
)
plt.close
print( "Points size follows one of the pd columns: ang_vel")
# Video('./pend-scatter.gif', html_attributes="controls muted autoplay")
# set up and generate animated bar race chart
#
# set up figure to pass onto `pandas_alive`
# NOTE:by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast!
fig2 = plt.Figure
ax2 = fig2.add_subplot
fig2.set_size_inches(figsize)
ax2.set_title( "Simple pendulum animation, L="+ str(L) + "m", fontsize= "medium")
ax2.set_xlabel( "Amplitudes", color= 'dimgray', fontsize=fontsize)
ax2.set_ylabel( "", color= 'dimgray', fontsize= "x-small")
ax2.tick_params(labelsize=fontsize)
# pandas_alive
race_chart = df.plot_animated(filename= "pend-race.gif", kind= 'race', period_label={ 'x': 0.05, 'y': 0.9},
steps_per_period= 1, interpolate_period= False, period_length= 50,
period_fmt= 'Time:{x:10.2f}',
enable_progress_bar= True, fixed_max= False, dpi= 100, fig=fig2
)
plt.close
# set up and generate bubble animated plot
#
# set up figure to pass onto `pandas_alive`
# NOTE:by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast!
fig3 = plt.Figure
ax3 = fig3.add_subplot
fig3.set_size_inches(figsize)
ax3.set_title( "Simple pendulum animation, L="+ str(L) + "m", fontsize= "medium")
ax3.set_xlabel( "Hor Displacement (m)", color= 'dimgray', fontsize=fontsize)
ax3.set_ylabel( "Ver Displacement (m)", color= 'dimgray', fontsize=fontsize)
# limits & ratio below get the graph square
ax3.set_xlim(-L * ax_scale, L * ax_scale)
ax3.set_ylim(-L * ax_scale - 0.1, L * ax_scale - 0.1)
ratio = 1.# this is visual ratio of axes
ax3.set_aspect(ratio / ax3.get_data_ratio)
ax3.arrow( 0, 0, df2[ "dx (m)"].iloc[ -1], df2[ "dy (m)"].iloc[ -1],
color= "dimgray", ls= ":", lw= 1, width= .0, head_width= 0, zorder= -1)
# pandas_alive
bubble_chart = df2.plot_animated(
kind= "bubble", filename= "pend-bubble.gif",
x_data_label= "dx (m)", y_data_label= "dy (m)",
size_data_label= "size", color_data_label= "ang_vel", cmap= "jet",
period_label={ 'x': 0.05, 'y': 0.9}, vmin= None, vmax= None,
steps_per_period= 1, interpolate_period= False, period_length= 50, period_fmt= 'Time:{x:10.2f}s',
enable_progress_bar= True, fixed_max= False, dpi=dpi, fig=fig3
)
plt.close
print( "Bubble size & colour animates with pd data column for ang_vel.")
# Combined plots
#
fontsize = "x-small"
# Otherwise titles overlap and subplots_adjust does nothing
frommatplotlib importrcParams
rcParams.update({ "figure.autolayout": False})
figs = plt.Figure(figsize=( 9, 4), dpi= 100)
figs.subplots_adjust(wspace= 0.1)
gs = figs.add_gridspec( 2, 2)
ax1 = figs.add_subplot(gs[ 0, 0])
ax1.set_xlabel( "Time(s)", color= 'dimgray', fontsize=fontsize)
ax1.set_ylabel( "Amplitudes", color= 'dimgray', fontsize=fontsize)
ax1.tick_params(labelsize=fontsize)
ax2 = figs.add_subplot(gs[ 1, 0])
ax2.set_xlabel( "Amplitudes", color= 'dimgray', fontsize=fontsize)
ax2.set_ylabel( "", color= 'dimgray', fontsize=fontsize)
ax2.tick_params(labelsize=fontsize)
ax3 = figs.add_subplot(gs[:, 1])
ax3.set_xlabel( "Hor Displacement (m)", color= 'dimgray', fontsize=fontsize)
ax3.set_ylabel( "Ver Displacement (m)", color= 'dimgray', fontsize=fontsize)
ax3.tick_params(labelsize=fontsize)
# limits & ratio below get the graph square
ax3.set_xlim(-L * ax_scale, L * ax_scale)
ax3.set_ylim(-L * ax_scale - 0.1, L * ax_scale - 0.1)
ratio = 1.# this is visual ratio of axes
ax3.set_aspect(ratio / ax3.get_data_ratio)
line_chart.ax = ax1
race_chart.ax = ax2
bubble_chart.ax = ax3
plots = [line_chart, race_chart, bubble_chart]
# pandas_alive combined using custom figure
pandas_alive.animate_multiple_plots(
filename= 'pend-combined.gif', plots=plots, custom_fig=figs, dpi= 100, enable_progress_bar= True,
adjust_subplot_left= 0.2, adjust_subplot_right= None,
title= "Simple pendulum animations, L="+ str(L) + "m", title_fontsize= "medium"
)
plt.close
最后如果你想完成 中文動態圖表的制作，加入中文顯示代碼即可。
# 中文顯示
plt.rcParams[ 'font.sans-serif'] = [ 'SimHei'] # Windows
plt.rcParams[ 'font.sans-serif'] = [ 'Hiragino Sans GB'] # Mac
plt.rcParams[ 'axes.unicode_minus'] = False
# 讀取數據
df_result = pd.read_csv( 'data/yuhuanshui.csv', index_col= 0, parse_dates=[ 0])
# 生成圖表
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= '我是余歡水演職人員熱度排行')
還是使用演員的 百度指數數據。
下載方式
我把 CSV文件及相關代碼進行打包共享了，需要的同學可以后臺回復關鍵字 「動態圖」獲取~返回搜狐，查看更多
責任編輯：
總結
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