商务统计_6 用图表演示数据 - 定性数据
目录
- 定性数据
- 饼图
- 条形图
定性数据
- 饼图(pie chart)
多个扇形组成的圆，每个扇形的大小比例为各类别的相对频数或百分比。
通常表示定类、定序数据。- 优缺
优：直观；可以展示每类数据的占比
缺：不能分类太多；不能比较组间数据；丢失部分数据，如最大值
- 优缺
# _*_ coding: utf-8 _*_
# Python
# 构造数据集
import math
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def lis():
# 随机整数序列 50个
np.random.seed(110)
return np.random.randint(low=70, high=130, size=50)
def group():
# 分多少组？
k = 1
while math.pow(2, k) < len(lis()):
k += 1
return k + 1
def gap_group():
# 组距？
if (max(lis()) - min(lis())) % group() == 0:
return (max(lis()) - min(lis()))//group()
else:
return (max(lis()) - min(lis()))//group() + 1
def lis_group_str():
# 节点值，上下限
return [min(lis()) + gap_group() * i for i in range(group() + 1)]
def flag2(df):
# 据节点值分组
for n, k in enumerate(lis_group_str()):
try:
df.loc[df['Random'] >= k, 'flag'] = str(n) + '-' + str(lis_group_str()[n]) + '-' + str(lis_group_str()[n+1])
except:
df.loc[df['Random'] >= k, 'flag'] = str(n-1) + '-' + str(lis_group_str()[n-1]) + '-' + str(lis_group_str()[n])
return df
df = flag2(pd.DataFrame(lis(), columns=['Random']))
help(pd.DataFrame.plot)
df.plot(x=None, y=None, kind=‘line’, ax=None, subplots=False, sharex=None, sharey=False, layout=None, figsize=None, use_index=True, title=None, grid=None, legend=True, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, secondary_y=False, sort_columns=False, **kwds)help(plt.pie)
pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, hold=None, data=None)
# autopct, explode
df.groupby(by='flag', as_index=True, sort=True).count().plot(y='Random', kind='pie', autopct='%.1f%%', explode=(0, 0.1, 0, 0, 0, 0, 0), figsize=(10, 8), title='Pie Demo', shadow=True)
plt.legend(loc=3)
plt.show()
-
条形图（水平、垂直、复式、堆积）
用等宽直条的长短来表示各指标大小。
适用于定类、定序数据。
优缺：- 优：直观；可以比较多个数据集。
- 缺：只适用于定性变量。
对比直方图：条形图可以不连续，直方图必须连续；条形图->定性数据，直方图->定量数据。
def df2():
dic = {'Things 1':{2001: 38, 2002: 40, 2003:43, 2004:42, 2005:46},
'Things 2':{2001: 40, 2002: 40, 2003:36, 2004:44, 2005:52},
'Things 3':{2001: 78, 2002: 73, 2003:77, 2004:43, 2005:40}}
return pd.DataFrame(dic)
df1 = df.groupby(by='flag', as_index=True, sort=True).count()
fig = plt.figure(figsize=(10, 12), dpi=80)
ax1 = fig.add_subplot(2, 3, 1)
df1.plot(ax=ax1, kind='bar', y='Random', use_index=True, title='Bar Demo', align='center', fontsize=6)
ax2 = fig.add_subplot(2, 3, 2)
df1.plot(ax=ax2, kind='barh', y='Random', use_index=True, title='Bar Demo', align='center')
ax3 = fig.add_subplot(2, 3, 3)
df2().plot(ax=ax3, kind='bar', use_index=True, title='Clustered bar')
ax4 = fig.add_subplot(2, 3, 4)
df2().plot(ax=ax4, kind='barh', use_index=True, title='Clustered barh')
ax5 = fig.add_subplot(2, 3, 5)
df2().plot(ax=ax5, kind='bar', use_index=True, stacked=True, title='Stacked bar')
ax6 = fig.add_subplot(2, 3, 6)
df2().plot(ax=ax6, kind='barh', use_index=True, stacked=True, title='Stacked barh')
plt.show()
- 附1. df.plot()参数
- 附2.
plt.bar
>>> help(plt.bar)
Help on function bar in module matplotlib.pyplot:
bar(*args, **kwargs)
Make a bar plot.
Call signatures::
bar(x, height, *, align='center', **kwargs)
bar(x, height, width, *, align='center', **kwargs)
bar(x, height, width, bottom, *, align='center', **kwargs)
The bars are positioned at *x* with the given *align* ment. Their
dimensions are given by *width* and *height*. The vertical baseline
is *bottom* (default 0).
Each of *x*, *height*, *width*, and *bottom* may either be a scalar
applying to all bars, or it may be a sequence of length N providing a
separate value for each bar.
Parameters
----------
x : sequence of scalars
The x coordinates of the bars. See also *align* for the
alignment of the bars to the coordinates.
height : scalar or sequence of scalars
The height(s) of the bars.
width : scalar or array-like, optional
The width(s) of the bars (default: 0.8).
bottom : scalar or array-like, optional
The y coordinate(s) of the bars bases (default: 0).
align : {'center', 'edge'}, optional, default: 'center'
Alignment of the bars to the *x* coordinates:
- 'center': Center the base on the *x* positions.
- 'edge': Align the left edges of the bars with the *x* positions.
To align the bars on the right edge pass a negative *width* and
``align='edge'``.
Returns
-------
`.BarContainer`
Container with all the bars and optionally errorbars.
Other Parameters
----------------
color : scalar or array-like, optional
The colors of the bar faces.
edgecolor : scalar or array-like, optional
The colors of the bar edges.
linewidth : scalar or array-like, optional
Width of the bar edge(s). If 0, don't draw edges.
tick_label : string or array-like, optional
The tick labels of the bars.
Default: None (Use default numeric labels.)
xerr, yerr : scalar or array-like of shape(N,) or shape(2,N), optional
If not *None*, add horizontal / vertical errorbars to the bar tips.
The values are +/- sizes relative to the data:
- scalar: symmetric +/- values for all bars
- shape(N,): symmetric +/- values for each bar
- shape(2,N): separate + and - values for each bar
Default: None
ecolor : scalar or array-like, optional, default: 'black'
The line color of the errorbars.
capsize : scalar, optional
The length of the error bar caps in points.
Default: None, which will take the value from
:rc:`errorbar.capsize`.
error_kw : dict, optional
Dictionary of kwargs to be passed to the `~.Axes.errorbar`
method. Values of *ecolor* or *capsize* defined here take
precedence over the independent kwargs.
log : bool, optional, default: False
If *True*, set the y-axis to be log scale.
orientation : {'vertical', 'horizontal'}, optional
*This is for internal use only.* Please use `barh` for
horizontal bar plots. Default: 'vertical'.