今天小編來(lái)演示一下如何用pandas一行代碼來(lái)繪制可以動(dòng)態(tài)交互的圖表，并且將繪制的圖表組合到一起，組成可視化大屏，本次小編將要繪制的圖表有

• 折線圖
• 散點(diǎn)圖
• 直方圖
• 柱狀圖
• 餅圖
• 面積圖
• 地圖
• 組合圖

準(zhǔn)備工作

我們先導(dǎo)入需要用到的庫(kù)，并做相應(yīng)的設(shè)置

import?pandas?as?pd
import?pandas_bokeh
pandas_bokeh.output_notebook()

因?yàn)樾【幨窃?code style="font-size: 14px;padding: 2px 4px;border-radius: 4px;margin-right: 2px;margin-left: 2px;color: rgb(30, 107, 184);background-color: rgba(27, 31, 35, 0.05);font-family: "Operator Mono", Consolas, Monaco, Menlo, monospace;word-break: break-all;">jupyter nobteook上面操作的，這邊就用到了output_notebook()的設(shè)置

折線圖

我們先來(lái)畫一張簡(jiǎn)單的折線圖，當(dāng)中隨機(jī)生成一批數(shù)據(jù)

import?numpy?as?np

np.random.seed(55)
df?=?pd.DataFrame({"寧德時(shí)代":?np.random.randn(100)+0.2,?
???????????????????"貴州茅臺(tái)":?np.random.randn(100)+0.17},?
???????????????????index=pd.date_range('1/1/2021',?periods=100))
df?=?df.cumsum()
df?=?df?+?50
df.plot_bokeh(kind="line")?

output

df.plot_bokeh.line(
????figsize=(800,?450),
????title="寧德時(shí)代?vs?貴州茅臺(tái)",
????xlabel="日期",
????ylabel="股票價(jià)格?[$]",
????yticks=[0,?100,?200,?300,?400],
????ylim=(0,?100),
????xlim=("2021-01-01",?"2021-04-01"),
????colormap=["red",?"blue"],
????plot_data_points=True,
????plot_data_points_size=10,
????marker="asterisk")

output

ts?=?pd.Series(np.random.randn(100),?index=pd.date_range('1/1/2021',?periods=100))
df?=?pd.DataFrame(np.random.randn(100,?4),?index=ts.index,?columns=list('ABCD'))
df?=?df.cumsum()

df.plot_bokeh(rangetool=True)

output

x?=?np.arange(-5,?5,?0.1)
y2?=?x**2
y3?=?x**3
df?=?pd.DataFrame({"x":?x,?"Type?1":?y2,?"Type?2":?y3})
df.plot_bokeh.point(
????x="x",
????xticks=range(-5,?5),
????size=5,
????colormap=["#009933",?"#ff3399"],
????title="折線圖?(Type?1?vs.?Type?2)",
????marker="x")

output

散點(diǎn)圖

接下來(lái)我們來(lái)看散點(diǎn)圖，步驟與上述的折線圖相類似

df?=?pd.read_csv("iris.csv")
p_scatter?=?df.plot_bokeh.scatter(
????x="petal?length(cm)",
????y="sepal?width(cm)",
????category="species",
????title="Iris數(shù)據(jù)集可視化",
????show_figure=True,
)

output

我們也可以通過(guò)當(dāng)中size這個(gè)參數(shù)來(lái)控制散點(diǎn)的大小，例如

df.loc[13,?"sepal?length(cm)"]?=?15
df.loc[15,?"sepal?length(cm)"]?=?17
df.loc[20,?"sepal?length(cm)"]?=?30
df.loc[40,?"sepal?length(cm)"]?=?20

p_scatter?=?df.plot_bokeh.scatter(
????x="petal?length(cm)",
????y="sepal?width(cm)",
????category="species",
????title="Iris數(shù)據(jù)集可視化",
????show_figure=True,
????size="sepal?length(cm)"
)

output

柱狀圖

下面我們來(lái)看一下直方圖的繪制

data?=?{
????'fruits':
????['蘋果',?'梨',?'草莓',?'西瓜',?'葡萄',?'香蕉'],
????'2015':?[2,?1,?4,?3,?2,?4],
????'2016':?[5,?3,?3,?2,?4,?6],
????'2017':?[3,?2,?4,?4,?5,?3]
}
df?=?pd.DataFrame(data).set_index("fruits")

p_bar?=?df.plot_bokeh.bar(
????ylabel="每斤的的價(jià)格?[￥]",?
????title="水果每年的價(jià)格",?
????alpha=0.6)

output

p_stacked_bar?=?df.plot_bokeh.bar(
????ylabel="每斤的的價(jià)格?[￥]",
????title="水果每年的價(jià)格",
????stacked=True,
????alpha=0.6)

output

直方圖

繪制直方圖的方式也是類似的

p_hist?=?df_hist.plot_bokeh.hist(
????y=["a",?"b"],
????bins=np.arange(-5,?5,?0.5),
????normed=100,
????vertical_xlabel=True,
????ylabel="Share[%]",
????title="正則分布直方圖",
????show_average=True,
????xlim=(-4,?6),
????ylim=(0,?30),
????show_figure=True)

output

小編個(gè)人覺(jué)得直方圖有點(diǎn)丑，不知道大家是不是有類似的體驗(yàn)

面積圖

df.plot_bokeh.area(
????x="Year",
????stacked=True,
????legend="top_left",
????colormap=["yellow",?"orange",?"black",?"grey",?"blue",?"green"],
????title="全球不同能源的消耗量",
????ylabel="不同能源的消耗(噸)",
????ylim=(0,?16000))

output

df.plot_bokeh.area(
????x="Year",
????stacked=True,
????normed?=?100,
????legend="bottom_left",
????colormap=["yellow",?"orange",?"black",?"grey",?"blue",?"green"],
????title="全球不同能源的消耗量",
????ylabel="不同能源的消耗(噸)")

output

餅圖

df_pie.plot_bokeh.pie(
????x="Type",
????y="2017",
????colormap=["blue",?"red",?"yellow",?"green",?"purple",?"orange",?"grey"],
????title="餅圖",
????)

output

df_pie.plot_bokeh.pie(
????x="Type",
????colormap=["blue",?"red",?"yellow",?"green",?"purple",?"orange",?"grey"],
????title="多重餅圖",
????line_color="black")

output

地圖

df_mapped.plot_bokeh.map(
????x="longitude",
????y="latitude",
????hovertool_string="""
?@{name}?
?
????
????????????????????????
?Population:?@{pop_max}?
""",
????tile_provider="STAMEN_TERRAIN_RETINA",
????size="population",?
????figsize=(900,?600),
????title="全球特大城市分布")

output

import?geopandas?as?gpd
import?pandas_bokeh
pandas_bokeh.output_file("Interactive?Plot.html")

df_states?=?gpd.read_file("states.geojson")
print(df_states.head())

df_states.plot_bokeh(
????figsize=(900,?600),
????category="POPESTIMATE2017",
????simplify_shapes=5000,
????colormap="Inferno",
????colormap_uselog=True,
????colorbar_tick_format="0.0a")

output

當(dāng)然我們也可以在地圖上面添加一個(gè)時(shí)間軸，讓圖表隨著時(shí)間的流逝而變化

for?i?in?range(8):
????df_states["Delta_Population_201%d"%i]?=?((df_states["POPESTIMATE201%d"%i]?/?df_states["POPESTIMATE2010"])?-1?)?*?100
????
slider_columns?=?["Delta_Population_201%d"%i?for?i?in?range(8)]
slider_range?=?range(2010,?2018)
df_states.plot_bokeh(
????figsize=(900,?600),
????simplify_shapes=5000,
????slider=slider_columns,
????slider_range=slider_range,
????slider_name="Year",
????colormap="Inferno",
????hovertool_columns=["STATE_NAME"]?+?slider_columns,
????title="Change?of?Population?[%]")

output

同時(shí)我們也可以在地圖上面添加一個(gè)下拉框，通過(guò)點(diǎn)選來(lái)篩選數(shù)據(jù)的展示

df_states["STATE_NAME_SMALL"]?=?df_states["STATE_NAME"].str.lower()

df_states.plot_bokeh(
????figsize=(900,?600),
????simplify_shapes=5000,
????dropdown=["POPESTIMATE2010",?"POPESTIMATE2017"],
????colormap="Viridis",
????hovertool_string="""
????????????????????????"?
????????????????????????height="42"?alt="@imgs"?width="42"
????????????????????????style="float:?left;?margin:?0px?15px?15px?0px;"
????????????????????????border="2">
????????????????
????????????????????????
??@STATE_NAME?

????????????????????????
?2010:?@POPESTIMATE2010?

????????????????????????
?2017:?@POPESTIMATE2017?
""",
????tile_provider_url=r"http://c.tile.stamen.com/watercolor/{Z}/{X}/{Y}.jpg",
????tile_attribution='Map?tiles?by?Stamen?Design,?under?CC?BY?3.0.?Data?by?OpenStreetMap,?under?ODbL.'
????)

output

df_states.plot_bokeh(
????figsize=(900,?600),
????simplify_shapes=5000,
????category="REGION",
????show_colorbar=False,
????colormap=["blue",?"yellow",?"green",?"red"],
????hovertool_columns=["STATE_NAME",?"REGION"],
????tile_provider="STAMEN_TERRAIN_RETINA")

多圖組合

#繪制出大致的輪廓圖
figure?=?df_states.plot_bokeh(
????figsize=(800,?450),
????simplify_shapes=10000,
????show_figure=False,
????xlim=[-170,?-80],
????ylim=[10,?70],
????category="REGION",
????colormap="Dark2",
????legend="States",
????show_colorbar=False,
)

#繪制人口的密度圖
df_cities.plot_bokeh(
????figure=figure,?????????#?<==?pass?figure?here!
????category="pop_max",
????colormap="Viridis",
????colormap_uselog=True,
????size="size",
????hovertool_string="""
@name

????????????????????????
Population:?@pop_max?
""",
????marker="inverted_triangle",
????legend="Cities",
)

df?=?pd.read_csv("iris.csv")

from?bokeh.models.widgets?import?DataTable,?TableColumn
from?bokeh.models?import?ColumnDataSource

data_table?=?DataTable(
????columns=[TableColumn(field=Ci,?title=Ci)?for?Ci?in?df.columns],
????source=ColumnDataSource(df),
????height=300,
)

#?創(chuàng)建散點(diǎn)圖:
p_scatter?=?df.plot_bokeh.scatter(
????x="petal?length(cm)",
????y="sepal?width(cm)",
????category="species",
????title="Iris數(shù)據(jù)可視化",
????show_figure=False,
)

#?Combine?Table?and?Scatterplot?via?grid?layout:
pandas_bokeh.plot_grid([[data_table,?p_scatter]],?plot_width=400,?plot_height=350)

output

我們也可以借此多繪制幾個(gè)直方圖，然后組合起來(lái)

#重置表格的行索引:
df.reset_index(inplace=True)

#創(chuàng)建水平方向的直方圖:
p_hbar?=?df.plot_bokeh(
????kind="barh",
????x="fruits",
????xlabel="Price?per?Unit?[€]",
????title="Fruit?prices?per?Year",
????alpha=0.6,
????legend?=?"bottom_right",
????show_figure=False)

#創(chuàng)建堆疊式的柱狀圖:
p_stacked_hbar?=?df.plot_bokeh.barh(
????x="fruits",
????stacked=True,
????xlabel="Price?per?Unit?[€]",
????title="Fruit?prices?per?Year",
????alpha=0.6,
????legend?=?"bottom_right",
????show_figure=False)

#Plot?all?barplot?examples?in?a?grid:
pandas_bokeh.plot_grid([[p_bar,?p_stacked_bar],
????????????????????????[p_hbar,?p_stacked_hbar]],?
???????????????????????plot_width=450)

output