High-performance data structures (usually, "datasets" in the context of machine learning) and data analysis tools for the Python programming language, similar to R. Some tools are:

Installation

pip3 install pandas

Example

Create a table with data:



In [0]:

    
try:
    import numpy as np
except:
    !pip3 install numpy
    import numpy as np
    
try:
    import pandas as pd
except:
    !pip3 install pandas
    import pandas as pd



In [0]:

    
df = pd.DataFrame({'int_col' : [1, 2, 6, 8, -1],
                    'float_col' : [0.1, 0.2, 0.2, 10.1, None],
                    'str_col' : ['a', 'b', None, 'c', 'a']})
print(df)
df









    



   int_col  float_col str_col
0        1        0.1       a
1        2        0.2       b
2        6        0.2    None
3        8       10.1       c
4       -1        NaN       a






    Out[0]:







  
    
      
      int_col
      float_col
      str_col
    
  
  
    
      0
      1
      0.1
      a
    
    
      1
      2
      0.2
      b
    
    
      2
      6
      0.2
      None
    
    
      3
      8
      10.1
      c
    
    
      4
      -1
      NaN
      a

Arithmetic average of a column:



In [0]:

    
df2 = df.copy()
mean = df2['float_col'].mean()
mean









    Out[0]:





2.65

Replace undefined elements:



In [0]:

    
df3 = df['float_col'].fillna(mean)
df3









    Out[0]:





0     0.10
1     0.20
2     0.20
3    10.10
4     2.65
Name: float_col, dtype: float64

Create a table by means of columns:



In [0]:

    
df4 = pd.concat([df3, df['int_col'], df['str_col']], axis=1)
df4

Pandas

Table of Contents

Installation

Example