notebook.community

Edit and run



In [1]:

    
import pandas as pd



In [2]:

    
print(pd.__version__)



In [3]:

    
df = pd.DataFrame({'i': [0, 10, 200], 'f': [0, 0.9, 0.09],
                   's_i': ['0', '10', '200'], 's_f': ['0', '0.9', '0.09']})



In [4]:

    
print(df)









    



     i     f  s_i   s_f
0    0  0.00    0     0
1   10  0.90   10   0.9
2  200  0.09  200  0.09



In [5]:

    
print(df.dtypes)









    



i        int64
f      float64
s_i     object
s_f     object
dtype: object



In [6]:

    
print(df['i'].astype(str))









    



0      0
1     10
2    200
Name: i, dtype: object



In [7]:

    
print(df['f'].astype(str))









    



0     0.0
1     0.9
2    0.09
Name: f, dtype: object



In [8]:

    
print(df.astype(str))









    



     i     f  s_i   s_f
0    0   0.0    0     0
1   10   0.9   10   0.9
2  200  0.09  200  0.09



In [9]:

    
print(df.astype(str).dtypes)









    



i      object
f      object
s_i    object
s_f    object
dtype: object



In [10]:

    
print(df['i'].astype(float))









    



0      0.0
1     10.0
2    200.0
Name: i, dtype: float64



In [11]:

    
print(df['f'].astype(int))









    



0    0
1    0
2    0
Name: f, dtype: int64



In [12]:

    
print(df['s_i'].astype(int))









    



0      0
1     10
2    200
Name: s_i, dtype: int64



In [13]:

    
print(df['s_i'].astype(float))









    



0      0.0
1     10.0
2    200.0
Name: s_i, dtype: float64



In [14]:

    
print(df['s_f'].astype(float))









    



0    0.00
1    0.90
2    0.09
Name: s_f, dtype: float64



In [15]:

    
# print(df['s_f'].astype(int))
# ValueError: invalid literal for int() with base 10: '0.1'



In [16]:

    
print(df['s_f'].astype(float).astype(int))









    



0    0
1    0
2    0
Name: s_f, dtype: int64



In [17]:

    
df['i'] = df['i'].astype(str)
print(df)









    



     i     f  s_i   s_f
0    0  0.00    0     0
1   10  0.90   10   0.9
2  200  0.09  200  0.09



In [18]:

    
df['f_s'] = df['f'].astype(str)
print(df)









    



     i     f  s_i   s_f   f_s
0    0  0.00    0     0   0.0
1   10  0.90   10   0.9   0.9
2  200  0.09  200  0.09  0.09



In [19]:

    
print(df.dtypes)









    



i       object
f      float64
s_i     object
s_f     object
f_s     object
dtype: object



In [20]:

    
s_int = pd.Series([0xff, 0o77, 0b11])



In [21]:

    
print(s_int)









    



0    255
1     63
2      3
dtype: int64



In [22]:

    
print(s_int.map(bin))









    



0    0b11111111
1      0b111111
2          0b11
dtype: object



In [23]:

    
print(s_int.map(oct))









    



0    0o377
1     0o77
2      0o3
dtype: object



In [24]:

    
print(s_int.map(hex))









    



0    0xff
1    0x3f
2     0x3
dtype: object



In [25]:

    
print(s_int.map('{:b}'.format))









    



0    11111111
1      111111
2          11
dtype: object



In [26]:

    
print(s_int.map('{:#b}'.format))









    



0    0b11111111
1      0b111111
2          0b11
dtype: object



In [27]:

    
print(s_int.map('{:#010b}'.format))









    



0    0b11111111
1    0b00111111
2    0b00000011
dtype: object



In [28]:

    
df_str = pd.DataFrame({'bin': ['0b01', '0b10', '0b11'],
                       'oct': ['0o07', '0o70', '0o77'],
                       'hex': ['0x0f', '0xf0', '0xff'],
                       'dec': ['1', '10', '11']})



In [29]:

    
print(df_str)









    



    bin   oct   hex dec
0  0b01  0o07  0x0f   1
1  0b10  0o70  0xf0  10
2  0b11  0o77  0xff  11



In [30]:

    
print(df_str.dtypes)









    



bin    object
oct    object
hex    object
dec    object
dtype: object



In [31]:

    
# print(df_str['bin'].astype(int))
# ValueError: invalid literal for int() with base 10: '0b01'



In [32]:

    
print(df_str['bin'].map(lambda x: int(x, 2)))









    



0    1
1    2
2    3
Name: bin, dtype: int64



In [33]:

    
print(df_str['oct'].map(lambda x: int(x, 8)))









    



0     7
1    56
2    63
Name: oct, dtype: int64



In [34]:

    
print(df_str['hex'].map(lambda x: int(x, 16)))









    



0     15
1    240
2    255
Name: hex, dtype: int64



In [35]:

    
print(df_str.applymap(lambda x: int(x, 0)))









    



   bin  oct  hex  dec
0    1    7   15    1
1    2   56  240   10
2    3   63  255   11



In [36]:

    
print(df_str['dec'].map(lambda x: int(x, 2)))









    



0    1
1    2
2    3
Name: dec, dtype: int64



In [37]:

    
s_str_dec = pd.Series(['01', '10', '11'])



In [38]:

    
print(s_str_dec)









    



0    01
1    10
2    11
dtype: object



In [39]:

    
print(s_str_dec.astype(int))









    



0     1
1    10
2    11
dtype: int64



In [40]:

    
# print(s_str_dec.map(lambda x: int(x, 0)))
# ValueError: invalid literal for int() with base 0: '01'



In [41]:

    
print(df_str['oct'].map(lambda x: int(x, 8)).map(hex))









    



0     0x7
1    0x38
2    0x3f
Name: oct, dtype: object



In [42]:

    
s_str = pd.Series(['0', '10', 'xxx'])



In [43]:

    
print(s_str)









    



0      0
1     10
2    xxx
dtype: object



In [44]:

    
print(s_str.str.zfill(8))









    



0    00000000
1    00000010
2    00000xxx
dtype: object



In [45]:

    
print(s_str.str.rjust(8))









    



0           0
1          10
2         xxx
dtype: object



In [46]:

    
print(s_str.str.rjust(8, '_'))









    



0    _______0
1    ______10
2    _____xxx
dtype: object



In [47]:

    
print(s_str.str.center(8))









    



0       0    
1       10   
2      xxx   
dtype: object



In [48]:

    
print(s_str.str.center(8, '_'))









    



0    ___0____
1    ___10___
2    __xxx___
dtype: object



In [49]:

    
print(s_str.str.ljust(8))









    



0    0       
1    10      
2    xxx     
dtype: object



In [50]:

    
print(s_str.str.ljust(8, '_'))









    



0    0_______
1    10______
2    xxx_____
dtype: object



In [51]:

    
s_num = pd.Series([0, 10, 100])



In [52]:

    
# print(s_num.str.rjust(8, '_'))
# AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas



In [53]:

    
print(s_num.astype(str).str.rjust(8, '_'))









    



0    _______0
1    ______10
2    _____100
dtype: object



In [54]:

    
df = pd.DataFrame({'i': [0, 10, 100],
                   'f': [0.1234, 1.234, 12.34],
                   'round': [0.4, 0.5, 0.6]})



In [55]:

    
print(df)









    



     i        f  round
0    0   0.1234    0.4
1   10   1.2340    0.5
2  100  12.3400    0.6



In [56]:

    
print(df.dtypes)









    



i          int64
f        float64
round    float64
dtype: object



In [57]:

    
print(df['i'].map('{:08}'.format))









    



0    00000000
1    00000010
2    00000100
Name: i, dtype: object



In [58]:

    
print(df['i'].map('{:_<8}'.format))









    



0    0_______
1    10______
2    100_____
Name: i, dtype: object



In [59]:

    
print(df['i'].map('{:x}'.format))









    



0     0
1     a
2    64
Name: i, dtype: object



In [60]:

    
print(df['i'].map('{:#010b}'.format))









    



0    0b00000000
1    0b00001010
2    0b01100100
Name: i, dtype: object



In [61]:

    
print(df['f'].map('{:.2f}'.format))









    



0     0.12
1     1.23
2    12.34
Name: f, dtype: object



In [62]:

    
print(df['f'].map('{:.2g}'.format))









    



0    0.12
1     1.2
2      12
Name: f, dtype: object



In [63]:

    
print(df['f'].map('{:.2e}'.format))









    



0    1.23e-01
1    1.23e+00
2    1.23e+01
Name: f, dtype: object



In [64]:

    
print(df['f'].map('{:.2%}'.format))









    



0      12.34%
1     123.40%
2    1234.00%
Name: f, dtype: object



In [65]:

    
print(df['round'].map('{:.0f}'.format))









    



0    0
1    0
2    1
Name: round, dtype: object