关于pandas:Pandas入门教程四

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import pandas as pd
import numpy as np
df=pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                           'foo', 'bar', 'foo', 'foo'],
                   'B' : ['one', 'one', 'two', 'three',
                          'two', 'two', 'one', 'three'],
                   'C' : np.random.randn(8),
                   'D' : np.random.randn(8)})
print(df)

grouped=df.groupby('A')
print('-'*30)
print(grouped.count())
print('-'*30)
grouped=df.groupby(['A','B'])
print(grouped.count())


print('-'*30)
# 通过函数分组
def get_letter_type(letter):
    if letter.lower() in 'aeiou':
        return 'a'
    else:
        return 'b'

grouped=df.groupby(get_letter_type,axis=1)
print(grouped.count())

     A      B         C         D
0  foo    one  1.429387  0.643569
1  bar    one -0.858448 -0.213034
2  foo    two  0.375644  0.214584
3  bar  three  0.042284 -0.330481
4  foo    two -1.421967  0.768176
5  bar    two  1.293483 -0.399003
6  foo    one -1.101385 -0.236341
7  foo  three -0.852603 -1.718694
------------------------------
     B  C  D
A           
bar  3  3  3
foo  5  5  5
------------------------------
           C  D
A   B          
bar one    1  1
    three  1  1
    two    1  1
foo one    2  2
    three  1  1
    two    2  2
------------------------------
   a  b
0  1  3
1  1  3
2  1  3
3  1  3
4  1  3
5  1  3
6  1  3
7  1  3

se=pd.Series([1,2,3,4,5],[6,9,8,9,8])
print(se)

se.groupby(level=0)

6    1
9    2
8    3
9    4
8    5
dtype: int64





<pandas.core.groupby.generic.SeriesGroupBy object at 0x111b00040>

# 分组求和
grouped=se.groupby(level=0).sum()
print(grouped)

6    1
8    8
9    6
dtype: int64

df2=pd.DataFrame({'X':['A','B','A','B'],'Y':[1,2,3,4]})
print(df2)

# 按 X 分组, 并查问 A 列的数据
grp=df2.groupby('X').get_group('A')
print(grp)

   X  Y
0  A  1
2  A  3

arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
index=pd.MultiIndex.from_arrays(arrays,names=['first','second'])
print(index)

MultiIndex([('bar', 'one'),
            ('bar', 'two'),
            ('baz', 'one'),
            ('baz', 'two'),
            ('foo', 'one'),
            ('foo', 'two'),
            ('qux', 'one'),
            ('qux', 'two')],
           names=['first', 'second'])

s=pd.Series(np.random.randn(8),index=index)
print(s)

first  second
bar    one       0.120979
       two      -0.440384
baz    one       0.515106
       two      -0.019882
foo    one       1.149595
       two      -0.369984
qux    one      -0.930438
       two       0.146044
dtype: float64

# 分组求和
grouped=s.groupby(level='first')
print(grouped.sum())

first
bar   -0.319405
baz    0.495224
foo    0.779611
qux   -0.784394
dtype: float64

grouped=df.groupby(['A','B'])
print(grouped.size())

A    B    
bar  one      1
     three    1
     two      1
foo  one      2
     three    1
     two      2
dtype: int64

print(df)

     A      B         C         D
0  foo    one  1.429387  0.643569
1  bar    one -0.858448 -0.213034
2  foo    two  0.375644  0.214584
3  bar  three  0.042284 -0.330481
4  foo    two -1.421967  0.768176
5  bar    two  1.293483 -0.399003
6  foo    one -1.101385 -0.236341
7  foo  three -0.852603 -1.718694

print(grouped.describe().head())

              C                                                              \
          count      mean       std       min       25%       50%       75%   
A   B                                                                         
bar one     1.0 -0.858448       NaN -0.858448 -0.858448 -0.858448 -0.858448   
    three   1.0  0.042284       NaN  0.042284  0.042284  0.042284  0.042284   
    two     1.0  1.293483       NaN  1.293483  1.293483  1.293483  1.293483   
foo one     2.0  0.164001  1.789526 -1.101385 -0.468692  0.164001  0.796694   
    three   1.0 -0.852603       NaN -0.852603 -0.852603 -0.852603 -0.852603   

                        D                                                    \
                max count      mean       std       min       25%       50%   
A   B                                                                         
bar one   -0.858448   1.0 -0.213034       NaN -0.213034 -0.213034 -0.213034   
    three  0.042284   1.0 -0.330481       NaN -0.330481 -0.330481 -0.330481   
    two    1.293483   1.0 -0.399003       NaN -0.399003 -0.399003 -0.399003   
foo one    1.429387   2.0  0.203614  0.622191 -0.236341 -0.016364  0.203614   
    three -0.852603   1.0 -1.718694       NaN -1.718694 -1.718694 -1.718694

                75%       max  
A   B                          
bar one   -0.213034 -0.213034  
    three -0.330481 -0.330481  
    two   -0.399003 -0.399003  
foo one    0.423592  0.643569  
    three -1.718694 -1.718694

grouped=df.groupby('A')
grouped['C'].agg([np.sum,np.mean,np.std])

<div>
<style scoped>

.dataframe tbody tr th:only-of-type {vertical-align: middle;}

.dataframe tbody tr th {vertical-align: top;}

.dataframe thead th {text-align: right;}

</style>

	sum	mean	std
A
bar	0.477319	0.159106	1.080712
foo	-1.570925	-0.314185	1.188767

</div>

import pandas as pd
import numpy as np
s=pd.Series(['A','b','c','D',np.nan])
print(s)
# 转小写
print(s.str.lower())

# 转大写
print(s.str.upper())

# 每个字符的长度
print(s.str.len())

0      A
1      b
2      c
3      D
4    NaN
dtype: object
0      a
1      b
2      c
3      d
4    NaN
dtype: object
0      A
1      B
2      C
3      D
4    NaN
dtype: object
0    1.0
1    1.0
2    1.0
3    1.0
4    NaN
dtype: float64

index=pd.Index(['Index','ru','men'])

# 去掉空格
print(index.str.strip())

# 去掉右边的空格
print(index.str.lstrip())
# 去掉左边的空格
print(index.str.rstrip())

Index(['Index', 'ru', 'men'], dtype='object')
Index(['Index', 'ru', 'men'], dtype='object')
Index(['Index', 'ru', 'men'], dtype='object')

df=pd.DataFrame(np.random.randn(3,2),columns=['A a','B b'],index=range(3))
print(df)

        A a       B b
0  3.005273  0.486696
1  1.093889  1.054230
2 -2.846352  0.302465

# 列替换
print(df.columns.str.replace('','_'))

Index(['A_a', 'B_b'], dtype='object')

s=pd.Series(['a_b_C','c_d_e','f_g_h'])
print(s)

0    a_b_C
1    c_d_e
2    f_g_h
dtype: object

print(s.str.split('_'))

0    [a, b, C]
1    [c, d, e]
2    [f, g, h]
dtype: object

print(s.str.split('_',expand=True,n=1))

   0    1
0  a  b_C
1  c  d_e
2  f  g_h

s = pd.Series(['A','rumen','ru','rumen','xiao','zhan'])

print(s.str.contains('ru'))

0    False
1     True
2     True
3     True
4    False
5    False
dtype: bool

s=pd.Series(['a','a|b','a|c'])
print(s)

0      a
1    a|b
2    a|c
dtype: object

print(s.str.get_dummies(sep='|'))

s=pd.Series(np.arange(5),np.arange(5)[::-1],dtype='int64')

4    0
3    1
2    2
1    3
0    4
dtype: int64

print(s[s>2])

1    3
0    4
dtype: int64

# isin 查问索引在某个范畴
print(s.isin([1,3,4]))

4    False
3     True
2    False
1     True
0     True
dtype: bool

# 依据索引查问数据
print(s[s.isin([1,3,4])])

3    1
1    3
0    4
dtype: int64

# 结构一个联结索引的数据
s=pd.Series(np.arange(6),index=pd.MultiIndex.from_product([[1,2],['a','b','c']]))

print(s)

1  a    0
   b    1
   c    2
2  a    3
   b    4
   c    5
dtype: int64

print(s.iloc[s.index.isin([(1,'b'),(2,'c')])])

1  b    1
2  c    5
dtype: int64

# 结构一个工夫序列
dates=pd.date_range('20200920',periods=8)
print(dates)

DatetimeIndex(['2020-09-20', '2020-09-21', '2020-09-22', '2020-09-23',
               '2020-09-24', '2020-09-25', '2020-09-26', '2020-09-27'],
              dtype='datetime64[ns]', freq='D')

df=pd.DataFrame(np.random.randn(8,4),index=dates,columns=['A','B','C','D'])
print(df)

                   A         B         C         D
2020-09-20 -1.218522  2.067088  0.015009  0.158780
2020-09-21 -0.546837 -0.601178 -0.894882  0.172037
2020-09-22  0.189848 -0.910520  0.196186 -0.073495
2020-09-23 -0.566892  0.899193 -0.450925  0.633253
2020-09-24  0.038838  1.577004  0.580927  0.609050
2020-09-25  1.562094  0.020813 -0.618859 -0.515212
2020-09-26 -1.333947  0.275765  0.139325  1.124207
2020-09-27 -1.271748  1.082302  1.036805 -1.041206

# 查问 A 列数据
print(df['A'])

2020-09-20   -1.218522
2020-09-21   -0.546837
2020-09-22    0.189848
2020-09-23   -0.566892
2020-09-24    0.038838
2020-09-25    1.562094
2020-09-26   -1.333947
2020-09-27   -1.271748
Freq: D, Name: A, dtype: float64

# 查问小于 0 的数字, 大于 0 的值默认被置为 NaN
df.where(df<0)

<div>
<style scoped>

.dataframe tbody tr th:only-of-type {vertical-align: middle;}

.dataframe tbody tr th {vertical-align: top;}

.dataframe thead th {text-align: right;}

</style>

	A	B	C	D
2020-09-20	-1.218522	NaN	NaN	NaN
2020-09-21	-0.546837	-0.601178	-0.894882	NaN
2020-09-22	NaN	-0.910520	NaN	-0.073495
2020-09-23	-0.566892	NaN	-0.450925	NaN
2020-09-24	NaN	NaN	NaN	NaN
2020-09-25	NaN	NaN	-0.618859	-0.515212
2020-09-26	-1.333947	NaN	NaN	NaN
2020-09-27	-1.271748	NaN	NaN	-1.041206

</div>

# 查问小于 0 的数字, 大于 0 的值变成正数
print(df.where(df<0,-df))

                   A         B         C         D
2020-09-20 -1.218522 -2.067088 -0.015009 -0.158780
2020-09-21 -0.546837 -0.601178 -0.894882 -0.172037
2020-09-22 -0.189848 -0.910520 -0.196186 -0.073495
2020-09-23 -0.566892 -0.899193 -0.450925 -0.633253
2020-09-24 -0.038838 -1.577004 -0.580927 -0.609050
2020-09-25 -1.562094 -0.020813 -0.618859 -0.515212
2020-09-26 -1.333947 -0.275765 -0.139325 -1.124207
2020-09-27 -1.271748 -1.082302 -1.036805 -1.041206

# 查问小于 0 的数据, 大于 0 的置为 1000
print(df.where(df<0,1000))

                      A            B            C            D
2020-09-20    -1.218522  1000.000000  1000.000000  1000.000000
2020-09-21    -0.546837    -0.601178    -0.894882  1000.000000
2020-09-22  1000.000000    -0.910520  1000.000000    -0.073495
2020-09-23    -0.566892  1000.000000    -0.450925  1000.000000
2020-09-24  1000.000000  1000.000000  1000.000000  1000.000000
2020-09-25  1000.000000  1000.000000    -0.618859    -0.515212
2020-09-26    -1.333947  1000.000000  1000.000000  1000.000000
2020-09-27    -1.271748  1000.000000  1000.000000    -1.041206

# 结构一个 10 行 3 列的数据
df=pd.DataFrame(np.random.randn(10,3),columns=list('abc'))
print(df)

          a         b         c
0  1.761415  0.528009 -0.347271
1 -0.682149  0.353312  0.337229
2  1.080733 -0.272290  1.020335
3 -0.979681 -1.753745  0.836387
4  0.243748  2.085531 -0.993318
5 -1.041006  1.518130 -0.087383
6 -1.400354 -0.095196  3.043639
7 -0.835144  0.926415 -1.217102
8  0.326098  1.079906  0.156884
9  1.836618 -1.288516 -2.492620

# 查问 a >b 的数据
print(df.query('a>b'))

          a         b         c
0  1.761415  0.528009 -0.347271
2  1.080733 -0.272290  1.020335
3 -0.979681 -1.753745  0.836387
9  1.836618 -1.288516 -2.492620

# 查问 c >b>a 的数据
print(df.query('(c<b) & (b<a)'))

          a         b         c
0  1.761415  0.528009 -0.347271
9  1.836618 -1.288516 -2.492620

正文完

发表至： pandas

2020-09-28

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