Some pandas trick 2

Теги:

Create a pandas DataFrame from multiple dicts

>>> person1 = {'type': 01, 'name': 'Jhon', 'surname': 'Smith', 'phone': '555-1234'}
>>> person2 = {'type': 01, 'name': 'Jannette', 'surname': 'Jhonson', 'credit': 1000000.00}
>>> animal1 = {'type': 03, 'cname': 'cow', 'sciname': 'Bos....', 'legs': 4, 'tails': 1 }
>>> pd.DataFrame([person1])
   name     phone surname  type
0  Jhon  555-1234   Smith     1
>>> pd.DataFrame([person1, person2])
    credit      name     phone  surname  type
0      NaN      Jhon  555-1234    Smith     1
1  1000000  Jannette       NaN  Jhonson     1
>>> pd.DataFrame.from_dict([person1, person2])
    credit      name     phone  surname  type
0      NaN      Jhon  555-1234    Smith     1
1  1000000  Jannette       NaN  Jhonson     1

source

How to convert index of a pandas dataframe into a column

df['index1'] = df.index

# or
df = df.reset_index(level=0)

source, reset_index

How to get a value from a cell of a dataframe

In [3]: sub_df
Out[3]:
          A         B
2 -0.133653 -0.030854

In [4]: sub_df.iloc[0]
Out[4]:
A   -0.133653
B   -0.030854
Name: 2, dtype: float64

In [5]: sub_df.iloc[0]['A']
Out[5]: -0.13365288513107493

source

Assign new columns to a DataFrame

>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
                  index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

source

Chart Visualization in pandas

Pandas DataFrame Groupby two columns and get counts

>>> df = pd.DataFrame([[1.1, 1.1, 1.1, 2.6, 2.5, 3.4,2.6,2.6,3.4,3.4,2.6,1.1,1.1,3.3], list('AAABBBBABCBDDD'), [1.1, 1.7, 2.5, 2.6, 3.3, 3.8,4.0,4.2,4.3,4.5,4.6,4.7,4.7,4.8], ['x/y/z','x/y','x/y/z/n','x/u','x','x/u/v','x/y/z','x','x/u/v/b','-','x/y','x/y/z','x','x/u/v/w'],['1','3','3','2','4','2','5','3','6','3','5','1','1','1']]).T
>>> df.columns = ['col1','col2','col3','col4','col5']

>>> df
   col1 col2 col3     col4 col5
0   1.1    A  1.1    x/y/z    1
1   1.1    A  1.7      x/y    3
2   1.1    A  2.5  x/y/z/n    3
3   2.6    B  2.6      x/u    2
4   2.5    B  3.3        x    4
5   3.4    B  3.8    x/u/v    2
6   2.6    B    4    x/y/z    5
7   2.6    A  4.2        x    3
8   3.4    B  4.3  x/u/v/b    6
9   3.4    C  4.5        -    3
10  2.6    B  4.6      x/y    5
11  1.1    D  4.7    x/y/z    1
12  1.1    D  4.7        x    1
13  3.3    D  4.8  x/u/v/w    1
In [11]: df.groupby(['col5', 'col2']).size()
Out[11]:
col5  col2
1     A       1
      D       3
2     B       2
3     A       3
      C       1
4     B       1
5     B       2
6     B       1
dtype: int64
In [12]: df.groupby(['col5', 'col2']).size().groupby(level=1).max()
Out[12]:
col2
A       3
B       2
C       1
D       3
dtype: int64

source, groupby

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