正文

36 - Introduction to Pandas - Data reading and handling

  • 绘制直方图
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import pandas as pd

df = pd.read_csv('images/grains/grain_measurements.csv')
df['Area'].plot(kind='hist', title='Area', bins=50)
<AxesSubplot:title={'center':'Area'}, ylabel='Frequency'>

png

  • 创建 DataFrame, 修改 index 和 columns
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data = [[10, 200, 60],
        [12, 155, 45],
        [9, 50, -45.],
        [16, 240, 90]]

df = pd.DataFrame(data, index=[1, 2, 3, 4], columns=['Area', 'Intensity', 'Orientation'])
df
Area Intensity Orientation
1 10 200 60.0
2 12 155 45.0
3 9 50 -45.0
4 16 240 90.0

png

  • 查看各行缺失信息
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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Unnamed: 0  100 non-null    object 
 1   Image       100 non-null    object 
 2   Manual      94 non-null     float64
 3   Manual2     3 non-null      float64
 4   Auto_th_2   100 non-null    int64  
 5   Auto_th_3   100 non-null    int64  
 6   Auto_th_4   100 non-null    int64  
dtypes: float64(2), int64(3), object(2)
memory usage: 5.6+ KB
  • 查看表格行列数
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df.shape
(100, 7)
  • 查看整个表格
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df
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67
... ... ... ... ... ... ... ...
95 Set4 Image96 106.0 NaN 75 112 98
96 Set4 Image97 80.0 NaN 66 80 88
97 Set4 Image98 92.0 NaN 73 93 95
98 Set4 Image99 116.0 NaN 101 115 93
99 Set4 Image100 99.0 NaN 77 106 102

100 rows × 7 columns

  • 查看表格前 7 行
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df.head(7)
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67
5 Set1 Image6 115.0 NaN 82 124 105
6 Set1 Image7 102.0 NaN 68 103 93
  • 查看表格后 7 行
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df.tail(7)
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
93 Set4 Image94 81.0 NaN 65 90 70
94 Set4 Image95 NaN NaN 104 122 88
95 Set4 Image96 106.0 NaN 75 112 98
96 Set4 Image97 80.0 NaN 66 80 88
97 Set4 Image98 92.0 NaN 73 93 95
98 Set4 Image99 116.0 NaN 101 115 93
99 Set4 Image100 99.0 NaN 77 106 102
  • 将某一列改为 index
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df1 = df.set_index('Image')
df1.head()
Unnamed: 0 Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
Image
Image1 Set1 92.0 93.0 70 87 82
Image2 Set1 87.0 83.0 60 85 83
Image3 Set1 104.0 98.0 74 99 94
Image4 Set1 99.0 NaN 73 101 109
Image5 Set1 89.0 NaN 59 90 67
  • 查看 columns（列）名
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df1.columns
Index(['Unnamed: 0', 'Manual', 'Manual2', 'Auto_th_2', 'Auto_th_3',
       'Auto_th_4'],
      dtype='object')
  • 去重
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df['Unnamed: 0'].unique()
array(['Set1', 'Set2', 'Set3', 'Set4'], dtype=object)
  • 修改列名
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df1 = df.rename(columns={'Unnamed: 0': 'Image_set'})
df1.columns
Index(['Image_set', 'Image', 'Manual', 'Manual2', 'Auto_th_2', 'Auto_th_3',
       'Auto_th_4'],
      dtype='object')
  • 显示数据类型
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df.dtypes
Unnamed: 0     object
Image          object
Manual        float64
Manual2       float64
Auto_th_2       int64
Auto_th_3       int64
Auto_th_4       int64
dtype: object
  • 显示统计数据
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df.describe()
Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
count 94.000000 3.000000 100.000000 100.000000 100.000000
mean 100.021277 91.333333 76.370000 97.580000 93.210000
std 11.285140 7.637626 11.971055 12.327337 14.128769
min 80.000000 83.000000 55.000000 71.000000 63.000000
25% 90.250000 88.000000 67.750000 89.500000 83.750000
50% 101.000000 93.000000 74.500000 98.500000 93.000000
75% 108.000000 95.500000 85.000000 106.000000 103.250000
max 120.000000 98.000000 109.000000 124.000000 129.000000

37 - Introduction to Pandas - Data Manipulation

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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')
df.head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67
  • 删除某列
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df1 = df.drop('Manual2', axis=1)
df1.head()
Unnamed: 0 Image Manual Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 70 87 82
1 Set1 Image2 87.0 60 85 83
2 Set1 Image3 104.0 74 99 94
3 Set1 Image4 99.0 73 101 109
4 Set1 Image5 89.0 59 90 67
  • 删除多列
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df2 = df.drop(['Manual2', 'Auto_th_2'], axis=1)
df2.head()
Unnamed: 0 Image Manual Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 87 82
1 Set1 Image2 87.0 85 83
2 Set1 Image3 104.0 99 94
3 Set1 Image4 99.0 101 109
4 Set1 Image5 89.0 90 67
  • 添加并填充一列
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df['Date'] = '2019-06-24'
df.head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4 Date
0 Set1 Image1 92.0 93.0 70 87 82 2019-06-24
1 Set1 Image2 87.0 83.0 60 85 83 2019-06-24
2 Set1 Image3 104.0 98.0 74 99 94 2019-06-24
3 Set1 Image4 99.0 NaN 73 101 109 2019-06-24
4 Set1 Image5 89.0 NaN 59 90 67 2019-06-24 
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df.dtypes
Unnamed: 0     object
Image          object
Manual        float64
Manual2       float64
Auto_th_2       int64
Auto_th_3       int64
Auto_th_4       int64
Date           object
dtype: object
  • 将字符串转换成时间
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df['Date'] = pd.to_datetime('2019-06-24')
df.head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4 Date
0 Set1 Image1 92.0 93.0 70 87 82 2019-06-24
1 Set1 Image2 87.0 83.0 60 85 83 2019-06-24
2 Set1 Image3 104.0 98.0 74 99 94 2019-06-24
3 Set1 Image4 99.0 NaN 73 101 109 2019-06-24
4 Set1 Image5 89.0 NaN 59 90 67 2019-06-24 
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df.dtypes
Unnamed: 0            object
Image                 object
Manual               float64
Manual2              float64
Auto_th_2              int64
Auto_th_3              int64
Auto_th_4              int64
Date          datetime64[ns]
dtype: object
  • 保存成 .csv 文件
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df.to_csv('data/manual_vs_auto_updated.csv')
  • 删除某行
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df1 = df.drop(df.index[1])
df1.head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4 Date
0 Set1 Image1 92.0 93.0 70 87 82 2019-06-24
2 Set1 Image3 104.0 98.0 74 99 94 2019-06-24
3 Set1 Image4 99.0 NaN 73 101 109 2019-06-24
4 Set1 Image5 89.0 NaN 59 90 67 2019-06-24
5 Set1 Image6 115.0 NaN 82 124 105 2019-06-24
  • 删除前 10 行
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df1 = df.iloc[10:,]
df1.head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4 Date
10 Set1 Image11 91.0 NaN 61 87 77 2019-06-24
11 Set1 Image12 119.0 NaN 79 105 111 2019-06-24
12 Set1 Image13 NaN NaN 65 90 84 2019-06-24
13 Set1 Image14 117.0 NaN 94 115 105 2019-06-24
14 Set1 Image15 91.0 NaN 66 99 70 2019-06-24
  • 选取某些行
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df1 = df[df['Unnamed: 0'] != 'Set1']
df1.head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4 Date
25 Set2 Image26 102.0 NaN 85 103 105 2019-06-24
26 Set2 Image27 93.0 NaN 76 84 98 2019-06-24
27 Set2 Image28 83.0 NaN 62 71 87 2019-06-24
28 Set2 Image29 110.0 NaN 92 117 85 2019-06-24
29 Set2 Image30 89.0 NaN 70 96 81 2019-06-24

38 - Introduction to Pandas - Data Sorting

  • 排序
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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')
df2 = df.sort_values('Manual', ascending=True)  # ascending: 升序
  • 选取某行/列
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df2[['Manual', 'Auto_th_2']]
Manual Auto_th_2
34 80.0 58
96 80.0 66
93 81.0 65
66 81.0 65
44 82.0 67
... ... ...
32 NaN 66
59 NaN 74
79 NaN 69
82 NaN 64
94 NaN 104

100 rows × 2 columns

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df[20: 30]
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
20 Set1 Image21 89.0 NaN 65 94 86
21 Set1 Image22 88.0 NaN 66 96 83
22 Set1 Image23 106.0 NaN 71 112 105
23 Set1 Image24 107.0 NaN 92 91 111
24 Set1 Image25 108.0 NaN 93 113 115
25 Set2 Image26 102.0 NaN 85 103 105
26 Set2 Image27 93.0 NaN 76 84 98
27 Set2 Image28 83.0 NaN 62 71 87
28 Set2 Image29 110.0 NaN 92 117 85
29 Set2 Image30 89.0 NaN 70 96 81
  • loc 方法是通过行、列的名称或者标签来寻找我们需要的值。

Pandas 读取某列、某行数据——loc、iloc 用法总结_子木同学的博客-CSDN 博客_pandas iloc

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df.loc[20: 30, ['Manual', 'Auto_th_2']]
Manual Auto_th_2
20 89.0 65
21 88.0 66
22 106.0 71
23 107.0 92
24 108.0 93
25 102.0 85
26 93.0 76
27 83.0 62
28 110.0 92
29 89.0 70
30 115.0 77 
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set2_df = df[df['Unnamed: 0'] == 'Set2']
set2_df.head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
25 Set2 Image26 102.0 NaN 85 103 105
26 Set2 Image27 93.0 NaN 76 84 98
27 Set2 Image28 83.0 NaN 62 71 87
28 Set2 Image29 110.0 NaN 92 117 85
29 Set2 Image30 89.0 NaN 70 96 81
  • 选取最大值
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max(set2_df['Manual'])
120.0
  • 根据条件选取某些值
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df['Manual'] > 100
0     False
1     False
2      True
3     False
4     False
      ...  
95     True
96    False
97    False
98     True
99    False
Name: Manual, Length: 100, dtype: bool

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df[df['Manual'] > 100].head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
2 Set1 Image3 104.0 98.0 74 99 94
5 Set1 Image6 115.0 NaN 82 124 105
6 Set1 Image7 102.0 NaN 68 103 93
7 Set1 Image8 117.0 NaN 77 122 88
8 Set1 Image9 104.0 NaN 88 99 112
  • 复合条件
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df[(df['Manual'] > 100) & (df['Auto_th_2'] < 100)].head()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
2 Set1 Image3 104.0 98.0 74 99 94
5 Set1 Image6 115.0 NaN 82 124 105
6 Set1 Image7 102.0 NaN 68 103 93
7 Set1 Image8 117.0 NaN 77 122 88
8 Set1 Image9 104.0 NaN 88 99 112
  • 遍历某些行/列
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for index, row in df.iterrows():
    average_auto = (row['Auto_th_2'] + row['Auto_th_3'] + row['Auto_th_4']) / 3
    print(round(average_auto), row['Manual'])
80 92.0
76 87.0
89 104.0
94 99.0
72 89.0
104 115.0
88 102.0
96 117.0
100 104.0
87 103.0
75 91.0
98 119.0
80 nan
...

39 - Introduction to Pandas - Grouping Data

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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')
df = df.rename(columns = {'Unnamed: 0': 'Image_set'})
df.head()
Image_set Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67 
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df = df.drop('Manual2', axis=1)
df.head()
Image_set Image Manual Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 70 87 82
1 Set1 Image2 87.0 60 85 83
2 Set1 Image3 104.0 74 99 94
3 Set1 Image4 99.0 73 101 109
4 Set1 Image5 89.0 59 90 67
  • 以 Image_set 为分组做统计
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group_by_file = df.groupby(by=['Image_set'])
set_data_count = group_by_file.count()
set_data_avg = group_by_file.mean()
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set_data_count
Image Manual Auto_th_2 Auto_th_3 Auto_th_4
Image_set
Set1 25 24 25 25 25
Set2 25 24 25 25 25
Set3 25 24 25 25 25
Set4 25 22 25 25 25 
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set_data_avg
Manual Auto_th_2 Auto_th_3 Auto_th_4
Image_set
Set1 100.666667 72.84 98.04 92.36
Set2 98.666667 75.40 98.00 93.44
Set3 100.000000 78.48 95.52 94.40
Set4 100.818182 78.76 98.76 92.64
  • 统计关联性（手动与自动阈值）
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df['Manual'].corr(df['Auto_th_2'])
0.7381233054217538

40 - Introduction to Pandas - Dealing with missing -null- data

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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')
df.head(8)
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67
5 Set1 Image6 115.0 NaN 82 124 105
6 Set1 Image7 102.0 NaN 68 103 93
7 Set1 Image8 117.0 NaN 77 122 88
  • 查看缺失值
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df.isnull()
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 False False False False False False False
1 False False False False False False False
2 False False False False False False False
3 False False False True False False False
4 False False False True False False False
... ... ... ... ... ... ... ...
95 False False False True False False False
96 False False False True False False False
97 False False False True False False False
98 False False False True False False False
99 False False False True False False False

100 rows × 7 columns

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df.isnull().sum()
Unnamed: 0     0
Image          0
Manual         6
Manual2       97
Auto_th_2      0
Auto_th_3      0
Auto_th_4      0
dtype: int64
  • 删除缺失值
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df = df.drop('Manual2', axis=1)
df2 = df.dropna()
df2.head(10)
Unnamed: 0 Image Manual Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 70 87 82
1 Set1 Image2 87.0 60 85 83
2 Set1 Image3 104.0 74 99 94
3 Set1 Image4 99.0 73 101 109
4 Set1 Image5 89.0 59 90 67
5 Set1 Image6 115.0 82 124 105
6 Set1 Image7 102.0 68 103 93
7 Set1 Image8 117.0 77 122 88
8 Set1 Image9 104.0 88 99 112
9 Set1 Image10 103.0 69 98 94 
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df = pd.read_csv('data/manual_vs_auto.csv')
df.describe()
Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
count 94.000000 3.000000 100.000000 100.000000 100.000000
mean 100.021277 91.333333 76.370000 97.580000 93.210000
std 11.285140 7.637626 11.971055 12.327337 14.128769
min 80.000000 83.000000 55.000000 71.000000 63.000000
25% 90.250000 88.000000 67.750000 89.500000 83.750000
50% 101.000000 93.000000 74.500000 98.500000 93.000000
75% 108.000000 95.500000 85.000000 106.000000 103.250000
max 120.000000 98.000000 109.000000 124.000000 129.000000
  • 填充缺失值
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df['Manual'].fillna(100, inplace=True)
df.head(10)
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67
5 Set1 Image6 115.0 NaN 82 124 105
6 Set1 Image7 102.0 NaN 68 103 93
7 Set1 Image8 117.0 NaN 77 122 88
8 Set1 Image9 104.0 NaN 88 99 112
9 Set1 Image10 103.0 NaN 69 98 94
  • 使用平均值填充缺失值
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import numpy as np

df = pd.read_csv('data/manual_vs_auto.csv')
df['Manual'] = df.apply(
    lambda row: (round((row['Auto_th_2'] + row['Auto_th_3'] + row['Auto_th_3']) / 3))  # 平均值
    if np.isnan(row['Manual'])  # 如果是缺失值的话
    else row['Manual'], axis=1)  # 填充在 Manual 列上
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df.head(10)
Unnamed: 0 Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67
5 Set1 Image6 115.0 NaN 82 124 105
6 Set1 Image7 102.0 NaN 68 103 93
7 Set1 Image8 117.0 NaN 77 122 88
8 Set1 Image9 104.0 NaN 88 99 112
9 Set1 Image10 103.0 NaN 69 98 94

41 - Introduction to Pandas - Plotting

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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')
df = df.rename(columns={'Unnamed: 0': 'Image_set'})
df.head()
Image_set Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4
0 Set1 Image1 92.0 93.0 70 87 82
1 Set1 Image2 87.0 83.0 60 85 83
2 Set1 Image3 104.0 98.0 74 99 94
3 Set1 Image4 99.0 NaN 73 101 109
4 Set1 Image5 89.0 NaN 59 90 67
  • 绘制折线图
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df['Manual'].plot()
<AxesSubplot:>

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  • 绘制直方图
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# 类型 hist，分组 30，标题 Manual Count，图像大小 12 * 10
df['Manual'].plot(kind='hist', bins=30, title='Manual Count', figsize=(12, 10))
<AxesSubplot:title={'center':'Manual Count'}, ylabel='Frequency'>

png

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df['Manual'].rolling(3).mean().plot()
<AxesSubplot:>

png

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df['Manual'].describe()
count     94.000000
mean     100.021277
std       11.285140
min       80.000000
25%       90.250000
50%      101.000000
75%      108.000000
max      120.000000
Name: Manual, dtype: float64

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df['Manual'].plot(kind='box', figsize=(8, 6))
<AxesSubplot:>

png

  • 散点图
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df.plot(kind='scatter', x='Manual', y='Auto_th_2', title='Manual vs Auto 2')
<AxesSubplot:title={'center':'Manual vs Auto 2'}, xlabel='Manual', ylabel='Auto_th_2'>

png

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def cell_count(x):
    if x <= 100.0:
        return 'low'
    else:
        return 'high'
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df['cell_count_index'] = df['Manual'].apply(cell_count)
df.head()
Image_set Image Manual Manual2 Auto_th_2 Auto_th_3 Auto_th_4 cell_count_index
0 Set1 Image1 92.0 93.0 70 87 82 low
1 Set1 Image2 87.0 83.0 60 85 83 low
2 Set1 Image3 104.0 98.0 74 99 94 high
3 Set1 Image4 99.0 NaN 73 101 109 low
4 Set1 Image5 89.0 NaN 59 90 67 low 
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df.to_csv('data/manual_vs_auto2.csv')
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df.boxplot(column='Manual', by='cell_count_index')
<AxesSubplot:title={'center':'Manual'}, xlabel='cell_count_index'>

png

42 - Introduction to Seaborn Plotting in Python

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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')
df['Manual'].fillna(100, inplace=True)
df = df.rename(columns={'Unnamed: 0': 'Image_Set'})
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import seaborn as sns

sns.distplot(df['Manual'])
C:\Users\gzjzx\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)



<AxesSubplot:xlabel='Manual', ylabel='Density'>

png

  • sns.kdeplot()核密度估计图
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sns.kdeplot(df['Manual'], shade=True)
sns.kdeplot(df['Auto_th_2'], shade=True)
sns.kdeplot(df['Auto_th_3'], shade=True)
sns.kdeplot(df['Auto_th_4'], shade=True)
<AxesSubplot:xlabel='Manual', ylabel='Density'>

png

  • sns.jointplot() 双变量关系图
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sns.jointplot(x='Manual', y='Auto_th_2', data=df, kind='kde')
<seaborn.axisgrid.JointGrid at 0x212f9ad23d0>

png

  • sns.pairplot() 用来展示两两特征之间的关系
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sns.pairplot(df, x_vars=['Auto_th_2', 'Auto_th_3', 'Auto_th_4'], y_vars='Manual', height=6)
<seaborn.axisgrid.PairGrid at 0x212f9bd0fd0>

png

  • sns.lmplot() 展示线性关系
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sns.lmplot(x='Manual', y='Auto_th_2', data=df, order=1, hue='Image_Set')
<seaborn.axisgrid.FacetGrid at 0x212fa457f70>

png

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from scipy import stats

slope, intercept, r_value, p_value, std_err = stats.linregress(df['Manual'], df['Auto_th_2'])
slope, intercept, r_value, p_value, std_err
(0.772483189743971,
 -0.8937686381919718,
 0.7058094587729904,
 2.396963973676236e-16,
 0.07831918096230937)
  • sns.swarmplot() 分簇散点图
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df = pd.read_csv('data/manual_vs_auto2.csv')
df['Manual'].fillna(100, inplace=True)
df = df.rename(columns={'Unnamed: 0': 'Image_Set'})

sns.swarmplot(x='Image_Set', y='Manual', data=df, hue='cell_count_index', dodge=True)
<AxesSubplot:xlabel='Image_Set', ylabel='Manual'>

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  • sns.heatmap() 热图
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corr = df.loc[:,df.dtypes == 'int64'].corr() #Correlates all int64 columns
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, cmap=sns.diverging_palette(220, 10, as_cmap=True))
<AxesSubplot:>

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