函数解析

Help on function bar in module plotly.express._chart_types:
bar(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, hover_name=None, hover_data=None, custom_data=None, text=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, animation_frame=None, animation_group=None, category_orders={}, labels={}, color_discrete_sequence=None, color_discrete_map={}, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, orientation='v', barmode='relative', log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None)
        In a bar plot, each row of `data_frame` is represented as a rectangular
        mark.
    Parameters
    ----------
    data_frame: DataFrame or array-like or dict
        This argument needs to be passed for column names (and not keyword
        names) to be used. Array-like and dict are tranformed internally to a
        pandas DataFrame. Optional: if missing, a DataFrame gets constructed
        under the hood using the other arguments.
    x: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        position marks along the x axis in cartesian coordinates. For
        horizontal histograms, these values are used as inputs to `histfunc`.
    y: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        position marks along the y axis in cartesian coordinates. For vertical
        histograms, these values are used as inputs to `histfunc`.
    color: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        assign color to marks.
    facet_row: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        assign marks to facetted subplots in the vertical direction.
    facet_col: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        assign marks to facetted subplots in the horizontal direction.
    facet_col_wrap: int
        Maximum number of facet columns. Wraps the column variable at this
        width, so that the column facets span multiple rows. Ignored if 0, and
        forced to 0 if `facet_row` or a `marginal` is set.
    hover_name: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like appear in bold
        in the hover tooltip.
    hover_data: list of str or int, or Series or array-like, or dict
        Either a list of names of columns in `data_frame`, or pandas Series, or
        array_like objects or a dict with column names as keys, with values
        True (for default formatting) False (in order to remove this column
        from hover information), or a formatting string, for example ':.3f' or
        '|%a' or list-like data to appear in the hover tooltip or tuples with a
        bool or formatting string as first element, and list-like data to
        appear in hover as second element Values from these columns appear as
        extra data in the hover tooltip.
    custom_data: list of str or int, or Series or array-like
        Either names of columns in `data_frame`, or pandas Series, or
        array_like objects Values from these columns are extra data, to be used
        in widgets or Dash callbacks for example. This data is not user-visible
        but is included in events emitted by the figure (lasso selection etc.)
    text: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like appear in the
        figure as text labels.
    error_x: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        size x-axis error bars. If `error_x_minus` is `None`, error bars will
        be symmetrical, otherwise `error_x` is used for the positive direction
        only.
    error_x_minus: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        size x-axis error bars in the negative direction. Ignored if `error_x`
        is `None`.
    error_y: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        size y-axis error bars. If `error_y_minus` is `None`, error bars will
        be symmetrical, otherwise `error_y` is used for the positive direction
        only.
    error_y_minus: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        size y-axis error bars in the negative direction. Ignored if `error_y`
        is `None`.
    animation_frame: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        assign marks to animation frames.
    animation_group: str or int or Series or array-like
        Either a name of a column in `data_frame`, or a pandas Series or
        array_like object. Values from this column or array_like are used to
        provide object-constancy across animation frames: rows with matching
        `animation_group`s will be treated as if they describe the same object
        in each frame.
    category_orders: dict with str keys and list of str values (default `{}`)
        By default, in Python 3.6+, the order of categorical values in axes,
        legends and facets depends on the order in which these values are first
        encountered in `data_frame` (and no order is guaranteed by default in
        Python below 3.6). This parameter is used to force a specific ordering
        of values per column. The keys of this dict should correspond to column
        names, and the values should be lists of strings corresponding to the
        specific display order desired.
    labels: dict with str keys and str values (default `{}`)
        By default, column names are used in the figure for axis titles, legend
        entries and hovers. This parameter allows this to be overridden. The
        keys of this dict should correspond to column names, and the values
        should correspond to the desired label to be displayed.
    color_discrete_sequence: list of str
        Strings should define valid CSS-colors. When `color` is set and the
        values in the corresponding column are not numeric, values in that
        column are assigned colors by cycling through `color_discrete_sequence`
        in the order described in `category_orders`, unless the value of
        `color` is a key in `color_discrete_map`. Various useful color
        sequences are available in the `plotly.express.colors` submodules,
        specifically `plotly.express.colors.qualitative`.
    color_discrete_map: dict with str keys and str values (default `{}`)
        String values should define valid CSS-colors Used to override
        `color_discrete_sequence` to assign a specific colors to marks
        corresponding with specific values. Keys in `color_discrete_map` should
        be values in the column denoted by `color`.
    color_continuous_scale: list of str
        Strings should define valid CSS-colors This list is used to build a
        continuous color scale when the column denoted by `color` contains
        numeric data. Various useful color scales are available in the
        `plotly.express.colors` submodules, specifically
        `plotly.express.colors.sequential`, `plotly.express.colors.diverging`
        and `plotly.express.colors.cyclical`.
    range_color: list of two numbers
        If provided, overrides auto-scaling on the continuous color scale.
    color_continuous_midpoint: number (default `None`)
        If set, computes the bounds of the continuous color scale to have the
        desired midpoint. Setting this value is recommended when using
        `plotly.express.colors.diverging` color scales as the inputs to
        `color_continuous_scale`.
    opacity: float
        Value between 0 and 1. Sets the opacity for markers.
    orientation: str (default `'v'`)
        One of `'h'` for horizontal or `'v'` for vertical)
    barmode: str (default `'relative'`)
        One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
        bars are stacked above zero for positive values and below zero for
        negative values. In `'overlay'` mode, bars are drawn on top of one
        another. In `'group'` mode, bars are placed beside each other.
    log_x: boolean (default `False`)
        If `True`, the x-axis is log-scaled in cartesian coordinates.
    log_y: boolean (default `False`)
        If `True`, the y-axis is log-scaled in cartesian coordinates.
    range_x: list of two numbers
        If provided, overrides auto-scaling on the x-axis in cartesian
        coordinates.
    range_y: list of two numbers
        If provided, overrides auto-scaling on the y-axis in cartesian
        coordinates.
    title: str
        The figure title.
    template: str or dict or plotly.graph_objects.layout.Template instance
        The figure template name (must be a key in plotly.io.templates) or
        definition.
    width: int (default `None`)
        The figure width in pixels.
    height: int (default `None`)
        The figure height in pixels.
    Returns
    -------
        plotly.graph_objects.Figure

最简单的柱状图

源代码

#将plotly交互包导入
import plotly.express as px
#获取数据
data_canada = px.data.gapminder().query("country == 'Canada'")
#绘制图形
fig = px.bar(data_canada, x='year', y='pop')
#展示数据
fig.show()

个人分析

这个是最简单的一个绘制过程，导入软件包，获取数据，绘制图形以及最后展示图形。获取数据是利用了谷歌的一个数据，国内的网络可能会有些问题，但其实问题不大，本质上就是一个DataFrame，标准的pandas那种格式，下面把这组数据的情况展示下

可以直接导出，具体的命令如下

import plotly.express as px
import pandas as pd
data_canada= px.data.gapminder().query("country == 'Canada'")
data_canada.to_csv("data_canada")
fig = px.bar(data_canada, x='year', y='pop')
fig.show()

然后结果如下

最后一步便是保存图片了，也是很方便的

import plotly.express as px
import pandas as pd
data_canada= px.data.gapminder().query("country == 'Canada'")
data_canada.to_csv("test.csv")
fig = px.bar(data_canada, x='year', y='pop')
fig.show()
fig.write_image('figure.svg')

fig.write_image可以导出多种格式的图片(png,jpg,svg,pdf,eps)，包括矢量图，满足各类人群需求。
总的来说plotly从绘图到保存很方便，完美的接合pandas清洗数据。

自定义参数

源代码

import plotly.express as px
data = px.data.gapminder()
data_canada = data[data.country == 'Canada']
fig = px.bar(data_canada, x='year', y='pop',
             hover_data=['lifeExp', 'gdpPercap'], color='lifeExp',
             labels={'pop':'population of Canada'}, height=400)
fig.show()

图像

解读

可以看到柱状图的颜色是根据color参数来定义的，这个图是lifeExp；
hover_data是关于网页的，比如你把鼠标点到某个图上，会显示这两个值，输出文件是看不到的；
labels其实就是我们横纵坐标的值，这里是把pop数据值显示为更长的population of Canada。
width和hight就是设置图形的宽和高

堆叠柱状图

当很多Y公用一个X值时，便是堆叠柱状图。

源代码

import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill", color='time')
fig.show()

这个还是用的官方的数据，具体的数据可以按照上方的例子查看。

图片

解读

这种其实是个极端情况，有很多的Y值公用一个X值，实际中往往不是这种。

分组柱状图

源代码

# Change the default stacking
import plotly.express as px
fig = px.bar(df, x="sex", y="total_bill", color='smoker', barmode='group',
             height=400)
fig.show()

图片

解读

其实主要调整了下barmode参数，这个参数决定了柱状图的模式。