Shantanu's Blog

Database Consultant

June 12, 2018

 

pandas case study - 4

How to bucket the values for a given range?

Use numpy.select with DataFrame constructor:

m1 = df < 1
m2 = (df>1)&(df<10 p="">m3 = (df>10)&(df<50 p="">m4 = df>5

vals = list('NLMH')

df = pd.DataFrame(np.select([m1,m2,m3,m4], vals), index=df.index, columns=df.columns)

_____

pd.cut(df.stack(),[-1,1,10,50,np.inf],labels=list('NLMH')).unstack()


https://stackoverflow.com/questions/50605498/pandas-selecting-and-modifying-dataframe-based-on-even-more-complex-criteria

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pandas case study - 3

convert under_score separated text to JSON 

flat = {'X_a_one_eleven': 10,
        'X_a_one_twelve': 20, 
        'X_b_one_eleven': 30,
        'X_b_two_twelve': 40, 
        'Y_a_one_eleven': 50,
        'Y_a_two_twelve': 60,
        'Y_b_one_eleven': 70,
        'Y_b_two_twelve': 80}


def nest_dict(flat):
    result = {}
    for k, v in flat.items():
        _nest_dict_rec(k, v, result)
    return result

def _nest_dict_rec(k, v, out):
    k, *rest = k.split('_', 1)
    if rest:
        _nest_dict_rec(rest[0], v, out.setdefault(k, {}))
    else:
        out[k] = v

dd=nest_dict(flat)

import pprint
pprint.pprint(dd)

{'X': {'a': {'one': {'eleven': 10, 'twelve': 20}},
       'b': {'one': {'eleven': 30}, 'two': {'twelve': 40}}},
 'Y': {'a': {'one': {'eleven': 50}, 'two': {'twelve': 60}},
       'b': {'one': {'eleven': 70}, 'two': {'twelve': 80}}}}


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June 03, 2018

 

pandas case study - 2

Unpacking string and list into separate columns

df = pd.DataFrame([[0, 4, 'Abc', 456, '45.55, 65.66, 76.55'],
                   [2, 5.2, 'abc', 5, '34.54, 35.67'],
                   [0.2, 6, 'xyz', 65, '12.21, 5.6'],
                   [3, 4.1, 'Xbc', 23, 'abcd ,pr']], columns=['start', 'end', 'name','body_mass', 'budget'])

Unpacking comma separated values is easy if the comma separated values are mentioned as strings.

df.join(df.budget.str.split(pat=',',expand=True))

The problem is when you have a list.

df = pd.DataFrame([[0, 4, 'Abc', 456, [45.55, 65.66, 76.55]],
                   [2, 5.2, 'abc', 5, [34.54, 35.67]],
                   [0.2, 6, 'xyz', 65, [12.21, 5.6]],
                   [3, 4.1, 'Xbc', 23, ['abcd' ,'pr']]], columns=['start', 'end', 'name','body_mass', 'budget'])

As you can see, the list is treated as "object" but when you check the individual items, you can see it is a list and not string.

df.applymap(type)

This will create only two columns "x", and "Y". It will ignore the third column "Z" because there is only 1 value 76.55 and values in other rows is missing.

df.assign(**dict(zip('XYZ', zip(*df.budget))))

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