用python学概率与统计（第二章）描述性统计:表格法，图形法

最新推荐文章于 2025-06-08 07:00:00 发布

polarislove1113

最新推荐文章于 2025-06-08 07:00:00 发布

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分类专栏： python数据分析文章标签： python 数据统计 pandas matplotlib

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本文介绍了如何使用Python进行数据的汇总、频数分布分析及可视化展示，包括柱状图、饼图、茎叶图等图表的绘制方法，并通过实例展示了定量与定性数据的处理流程。

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频数分布

2.1汇总定性数据

柱状图

import numpy as np
import pandas as pd
from pandas import Series,DataFrame
import matplotlib.pyplot as plt
%pylab


df = pd.read_csv("E:\\data\\SoftDrink.csv")
df

df.describe()    

grouped_df= df.groupby("Brand Purchased")

group1 = grouped_df.size()

group2.index

group2.values

plt.bar(group2.index,group2.values,color = ['b','r','y','c','g'],)

plt.title('frequency of softdring')

plt.xlabel("soft drink")
plt.ylabel("frequency")

?plt.bar

plt.ylim(ymax = 25) #设置y轴区间

for a,b in zip(group2.index,group2.values):
    plt.text(a, b+0.05, '%.0f' % b, ha='center', va= 'bottom',fontsize=10) #设置显示每条柱的数字标注

饼状图

plt.pie(group2.values,labels =group2.index,autopct = '%1.1f%%',shadow = False,startangle =90 )
plt.axis("equal")
plt.title("pie",loc= 'right')

2.2汇总定量数据

为定义定量数据中组的概念，需要下列三个步骤
1.确定分组的数目，互不重叠
2。确定分组区间（近似吧（最大值-最小值）/组数
3。确定组限

df = pd.read_csv("E:\\data\\Audit.csv")
In [124]:

df['Audit Time'].values
Out[124]:
array([12, 15, 20, 22, 14, 14, 15, 27, 21, 18, 19, 18, 22, 33, 16, 18, 17,
       23, 28, 13], dtype=int64)
In [117]:

df.describe()
Out[117]:
Audit Time
count   20.000000
mean    19.250000
std 5.437443
min 12.000000
25% 15.000000
50% 18.000000
75% 22.000000
max 33.000000
In [126]:

AT
bins=[10,15,20,25,30,35]
AT=pd.cut(df['Audit Time'],bins)
AT
Out[126]:
0     (10, 15]
1     (10, 15]
2     (15, 20]
3     (20, 25]
4     (10, 15]
5     (10, 15]
6     (10, 15]
7     (25, 30]
8     (20, 25]
9     (15, 20]
10    (15, 20]
11    (15, 20]
12    (20, 25]
13    (30, 35]
14    (15, 20]
15    (15, 20]
16    (15, 20]
17    (20, 25]
18    (25, 30]
19    (10, 15]
Name: Audit Time, dtype: category
Categories (5, interval[int64]): [(10, 15] < (15, 20] < (20, 25] < (25, 30] < (30, 35]]
In [130]:

freqDisofAT = pd.value_counts(AT)
In [135]:
freqDisofAT = freqDisofAT.sort_index()
In [151]:

freqDisofAT
Out[151]:
(10, 15]    6
(15, 20]    7
(20, 25]    4
(25, 30]    2
(30, 35]    1
Name: Audit Time, dtype: int64

plt.style.use( 'ggplot') #ggplot风格
plt.hist(df['Audit Time'].values,bins,edgecolor = 'k')
plt.hist(df['Audit Time'].values,bins,edgecolor = 'k')
Out[65]:
(array([ 4.,  8.,  5.,  2.,  1.]),
 array([10, 15, 20, 25, 30, 35]),
 <a list of 5 Patch objects>)
In [12]:

import matplotlib

In [18]:

plt.ylabel ("频数",fontproperties = 'SimHei',fontsize = 14) #中文显示
Out[18]:
Text(55.8472,0.5,'频数')
In [19]:

)
plt.xlabel ("审计时间（天）",fontproperties = 'SimHei',fontsize = 14)
Out[19]:
Text(0.5,23.1922,'审计时间（天）')
plt.hist(df['Audit Time'].values,bins,normed=True, histtype='step', cumulative=True) #频数累计图 normed 转换成频数

2.3 茎叶图

plt.stem(df.sort_values(“Correct”))

2.4 交叉分组列表和散点图

df = pd.read_csv("E:\\data\\Restaurant.csv")
In [18]:

c
df.describe()
Out[18]:
Restaurant  Meal Price ($)
count   300.000000  300.000000
mean    150.500000  25.896667
std 86.746758   9.287636
min 1.000000    10.000000
25% 75.750000   19.000000
50% 150.500000  25.000000
75% 225.250000  32.000000
max 300.000000  48.000000
In [19]:

df
df
Out[19]:
Restaurant  Quality Rating  Meal Price ($)
0   1   Good    18
1   2   Very Good   22
2   3   Good    28
3   4   Excellent   38
4   5   Very Good   33
5   6   Good    28
6   7   Very Good   19
7   8   Very Good   11
8   9   Very Good   23
9   10  Good    13
10  11  Very Good   33
11  12  Very Good   44
12  13  Excellent   42
13  14  Excellent   34
14  15  Good    25
15  16  Good    22
16  17  Good    26
17  18  Excellent   17
18  19  Very Good   30
19  20  Good    19
20  21  Very Good   33
21  22  Very Good   22
22  23  Excellent   32
23  24  Excellent   33
24  25  Very Good   34
25  26  Very Good   38
26  27  Good    27
27  28  Good    27
28  29  Very Good   26
29  30  Very Good   34
... ... ... ...
270 271 Excellent   48
271 272 Very Good   33
272 273 Very Good   25
273 274 Very Good   34
274 275 Very Good   20
275 276 Excellent   36
276 277 Excellent   40
277 278 Good    13
278 279 Very Good   12
279 280 Very Good   27
280 281 Very Good   20
281 282 Excellent   30
282 283 Good    29
283 284 Very Good   22
284 285 Good    27
285 286 Very Good   20
286 287 Excellent   37
287 288 Very Good   27
288 289 Good    23
289 290 Good    16
290 291 Very Good   23
291 292 Very Good   24
292 293 Excellent   45
293 294 Good    14
294 295 Good    18
295 296 Good    17
296 297 Good    16
297 298 Good    15
298 299 Very Good   38
299 300 Very Good   31
300 rows × 3 columns
In [20]:

30,40,50
bins = (10,20,30,40,50)
In [31]:

cut_pd = pd.cut(df['Meal Price ($)'],bins,right = False)
In [32]:

cut_pd
cut_pd
Out[32]:
0      [10, 20)
1      [20, 30)
2      [20, 30)
3      [30, 40)
4      [30, 40)
5      [20, 30)
6      [10, 20)
7      [10, 20)
8      [20, 30)
9      [10, 20)
10     [30, 40)
11     [40, 50)
12     [40, 50)
13     [30, 40)
14     [20, 30)
15     [20, 30)
16     [20, 30)
17     [10, 20)
18     [30, 40)
19     [10, 20)
20     [30, 40)
21     [20, 30)
22     [30, 40)
23     [30, 40)
24     [30, 40)
25     [30, 40)
26     [20, 30)
27     [20, 30)
28     [20, 30)
29     [30, 40)
         ...   
270    [40, 50)
271    [30, 40)
272    [20, 30)
273    [30, 40)
274    [20, 30)
275    [30, 40)
276    [40, 50)
277    [10, 20)
278    [10, 20)
279    [20, 30)
280    [20, 30)
281    [30, 40)
282    [20, 30)
283    [20, 30)
284    [20, 30)
285    [20, 30)
286    [30, 40)
287    [20, 30)
288    [20, 30)
289    [10, 20)
290    [20, 30)
291    [20, 30)
292    [40, 50)
293    [10, 20)
294    [10, 20)
295    [10, 20)
296    [10, 20)
297    [10, 20)
298    [30, 40)
299    [30, 40)
Name: Meal Price ($), Length: 300, dtype: category
Categories (4, interval[int64]): [[10, 20) < [20, 30) < [30, 40) < [40, 50)]
In [109]:

cross = pd.crosstab(df["Quality Rating"],cut_pd,margins = True)
In [110]:

cross
cross
Out[110]:
Meal Price ($) [10, 20)    [20, 30)    [30, 40)    [40, 50)    All
Quality Rating                  
Excellent   2   14  28  22  66
Good    42  40  2   0   84
Very Good   34  64  46  6   150
All 78  118 76  28  300
In [112]:

cross.reindex(['Good', 'Very Good','Excellent',  'All'])
Out[112]:
Meal Price ($) [10, 20)    [20, 30)    [30, 40)    [40, 50)    All
Quality Rating                  
Good    42  40  2   0   84
Very Good   34  64  46  6   150
Excellent   2   14  28  22  66
All 78  118 76  28  300

pd.crosstab(df["Quality Rating"],cut_pd,margins = True,normalize = True)
###
Meal Price ($) [10, 20)    [20, 30)    [30, 40)    [40, 50)    All
Quality Rating                  
Excellent   0.006667    0.046667    0.093333    0.073333    0.22
Good    0.140000    0.133333    0.006667    0.000000    0.28
Very Good   0.113333    0.213333    0.153333    0.020000    0.50
All 0.260000    0.393333    0.253333    0.093333    1.00

散点图

df2 = pd.read_csv("E:\\data\\Scatter.csv")
df2
#   Observation x   y
0   1   -22 22
1   2   -33 49
2   3   2   8
3   4   29  -16
4   5   -13 10
5   6   21  -28
6   7   -13 27
7   8   -23 35
8   9   14  -5
9   10  3   -3
10  11  -37 48
11  12  34  -29
12  13  9   -18
13  14  -33 31
14  15  20  -16
15  16  -3  14
16  17  -15 18
17  18  12  17
18  19  -20 -11
19  20  -7  -22
plt.scatter(df2["x"],df2["y"])