41 Pandas读取Excel绘制直方图

直方图(Histogram)：
直方图是数值数据分布的精确图形表示，是一个连续变量（定量变量）的概率分布的估计，它是一种条形图。为了构建直方图，第一步是将值的范围分段，即将整个值的范围分成一系列间隔，然后计算每个间隔中有多少值。

1. 读取数据

波斯顿房价数据集

import pandas as pd
import numpy as np

df = pd.read_excel("./datas/boston-house-prices/housing.xlsx")

df

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
0	0.00632	18.0	2.31	0	0.538	6.575	65.2	4.0900	1	296	15.3	396.90	4.98	24.0
1	0.02731	0.0	7.07	0	0.469	6.421	78.9	4.9671	2	242	17.8	396.90	9.14	21.6
2	0.02729	0.0	7.07	0	0.469	7.185	61.1	4.9671	2	242	17.8	392.83	4.03	34.7
3	0.03237	0.0	2.18	0	0.458	6.998	45.8	6.0622	3	222	18.7	394.63	2.94	33.4
4	0.06905	0.0	2.18	0	0.458	7.147	54.2	6.0622	3	222	18.7	396.90	5.33	36.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	0.06263	0.0	11.93	0	0.573	6.593	69.1	2.4786	1	273	21.0	391.99	9.67	22.4
502	0.04527	0.0	11.93	0	0.573	6.120	76.7	2.2875	1	273	21.0	396.90	9.08	20.6
503	0.06076	0.0	11.93	0	0.573	6.976	91.0	2.1675	1	273	21.0	396.90	5.64	23.9
504	0.10959	0.0	11.93	0	0.573	6.794	89.3	2.3889	1	273	21.0	393.45	6.48	22.0
505	0.04741	0.0	11.93	0	0.573	6.030	80.8	2.5050	1	273	21.0	396.90	7.88	11.9

506 rows × 14 columns

df.info()

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 506 entries, 0 to 505
    Data columns (total 14 columns):
     #   Column   Non-Null Count  Dtype  
    ---  ------   --------------  -----  
     0   CRIM     506 non-null    float64
     1   ZN       506 non-null    float64
     2   INDUS    506 non-null    float64
     3   CHAS     506 non-null    int64  
     4   NOX      506 non-null    float64
     5   RM       506 non-null    float64
     6   AGE      506 non-null    float64
     7   DIS      506 non-null    float64
     8   RAD      506 non-null    int64  
     9   TAX      506 non-null    int64  
     10  PTRATIO  506 non-null    float64
     11  B        506 non-null    float64
     12  LSTAT    506 non-null    float64
     13  MEDV     506 non-null    float64
    dtypes: float64(11), int64(3)
    memory usage: 55.5 KB
    

df["MEDV"]

0      24.0
1      21.6
2      34.7
3      33.4
4      36.2
       ... 
501    22.4
502    20.6
503    23.9
504    22.0
505    11.9
Name: MEDV, Length: 506, dtype: float64

2. 使用matplotlib画直方图

matplotlib直方图文档：https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.pyplot.hist.html

import matplotlib.pyplot as plt
%matplotlib inline

plt.figure(figsize=(12, 5))
plt.hist(df["MEDV"], bins=100)
plt.show()

3. 使用pyecharts画直方图

pyecharts直方图文档：http://gallery.pyecharts.org/#/Bar/bar_histogram numpy直方图文档：https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html

from pyecharts import options as opts
from pyecharts.charts import Bar

# 需要自己计算有多少个间隔、以及每个间隔有多少个值
hist,bin_edges =  np.histogram(df["MEDV"], bins=100)

# 这是每个间隔的分割点
bin_edges

array([ 5.  ,  5.45,  5.9 ,  6.35,  6.8 ,  7.25,  7.7 ,  8.15,  8.6 ,
        9.05,  9.5 ,  9.95, 10.4 , 10.85, 11.3 , 11.75, 12.2 , 12.65,
       13.1 , 13.55, 14.  , 14.45, 14.9 , 15.35, 15.8 , 16.25, 16.7 ,
       17.15, 17.6 , 18.05, 18.5 , 18.95, 19.4 , 19.85, 20.3 , 20.75,
       21.2 , 21.65, 22.1 , 22.55, 23.  , 23.45, 23.9 , 24.35, 24.8 ,
       25.25, 25.7 , 26.15, 26.6 , 27.05, 27.5 , 27.95, 28.4 , 28.85,
       29.3 , 29.75, 30.2 , 30.65, 31.1 , 31.55, 32.  , 32.45, 32.9 ,
       33.35, 33.8 , 34.25, 34.7 , 35.15, 35.6 , 36.05, 36.5 , 36.95,
       37.4 , 37.85, 38.3 , 38.75, 39.2 , 39.65, 40.1 , 40.55, 41.  ,
       41.45, 41.9 , 42.35, 42.8 , 43.25, 43.7 , 44.15, 44.6 , 45.05,
       45.5 , 45.95, 46.4 , 46.85, 47.3 , 47.75, 48.2 , 48.65, 49.1 ,
       49.55, 50.  ])

len(bin_edges)

    101


# 这是间隔的计数
hist


    array([ 2,  1,  1,  0,  5,  2,  1,  6,  3,  0,  3,  3,  5,  3,  4,  6,  3,
            5, 14,  9,  9,  6, 11,  8,  6,  8,  6, 10,  9,  9, 15, 13, 20, 16,
           19, 10, 14, 19, 13, 15, 21, 16,  9, 12, 14,  1,  0,  4,  5,  2,  6,
            5,  5,  4,  3,  6,  2,  3,  4,  3,  4,  3,  6,  2,  1,  1,  5,  3,
            1,  4,  1,  3,  1,  1,  1,  0,  0,  1,  0,  0,  1,  1,  1,  1,  1,
            1,  2,  0,  1,  1,  0,  1,  1,  0,  0,  0,  2,  1,  0, 16],
          dtype=int64)



len(hist)



    100

对bin_edges的解释，为什么是101个？比hist计数多1个？

举例：如果bins是[1, 2, 3, 4]，那么会分成3个区间：[1, 2)、[2, 3)、[3, 4]；其中bins的第一个值是数组的最小值，bins的最后一个元素是数组的最大值

# 注意观察，min是bins的第一个值，max是bins的最后一个元素
df["MEDV"].describe()


    count    506.000000
    mean      22.532806
    std        9.197104
    min        5.000000
    25%       17.025000
    50%       21.200000
    75%       25.000000
    max       50.000000
    Name: MEDV, dtype: float64


# 查看bins每一个值和前一个值的差值，可以看到这是等分的数据
np.diff(bin_edges)


    array([0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
           0.45])


# 这些间隔的数目，刚好等于计数hist的数目
len(np.diff(bin_edges))

    100


# pyecharts的直方图使用bar实现
# 取bins[:-1]，意思是用每个区间的左边元素作为x轴的值
bar = (
    Bar()
    .add_xaxis([str(x) for x in bin_edges[:-1]])
    .add_yaxis("价格分布", [float(x) for x in hist], category_gap=0)
    .set_global_opts(
        title_opts=opts.TitleOpts(title="波斯顿房价-价格分布-直方图", pos_left="center"),
        legend_opts=opts.LegendOpts(is_show=False)
    )
)

bar.render_notebook()

小作业： 获取你们产品的销量数据、价格数据，提取得到一个一数组，画一个直方图看一下数据分布

本文使用文章同步助手同步

41 Pandas读取Excel绘制直方图