apply panda in () method described

1.apply () Description

Scope: pandas in Seriesand DataFrame
effect: By using apply()the method, we can call their own definition of the function, makes the code more clear structure, concise.

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2.apply () application process

If a Seriesand DataFramecall the apply()method, and then use their own definition of the function, which since the first parameter of the function definition, represent Seriesand DataFramethe next "latitude" .
For example, if it is DataFrame, then the argument is his every column. If yes Series, it is his every value.

(1) DataFrame, each parameter is a column of his
eg: computing a DataFramerespective field of skewness and kurtosis

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'key1':[1, 2, 3, 4, 5],
    'key2':[4, 5, 6, 2, 1]
})

def skew_kurt(x):
    print(x, type(x))
    skews = x.skew()
    kurts = x.kurt()
    return pd.Series([skews, kurts], index=['skew', 'kurt']) # Series的参数为Series，则会变为DataFrame，且参数变为列
print(df.apply(skew_kurt))


# 结果：
0    1
1    2
2    3
3    4
4    5
Name: key1, dtype: int64 <class 'pandas.core.series.Series'>
0    1
1    2
2    3
3    4
4    5
Name: key1, dtype: int64 <class 'pandas.core.series.Series'>
0    4
1    5
2    6
3    2
4    1
Name: key2, dtype: int64 <class 'pandas.core.series.Series'>
      key1      key2
skew   0.0 -0.235514
kurt  -1.2 -1.963223

Can be seen by the output of the function will be executed many times, and every time the execution DataFrameof a biography in the past.
(2) Series, each parameter value of his
eg: one Seriesfor each value of the first character is replaced with that value

import pandas as pd
import numpy as np

s = pd.Series(['wang', 'li', 'zhao'])

def text(x):
    print(x, type(x))
    return x[0] # Series的参数为Series，则会变为DataFrame，且参数变为列
print(s.apply(text))

# 结果：
wang <class 'str'>
li <class 'str'>
zhao <class 'str'>
0    w
1    l
2    z
dtype: object

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3.apply () Application

(1) above by way of example, we can see that it can be applied Series, and DataFrame
(2) apply()method may also be applied to the packet --- groupby(). Also represents the next parameter latitude .
eg:

import pandas as pd
import numpy as np

df = pd.DataFrame({'data1':np.random.rand(5),
                  'data2':np.random.rand(5),
                  'key1':list('aabba'),
                  'key2':['one','two','one','two','one']})

print(df.groupby('key1').apply(lambda x: x.describe()))

# 结果：
a    count  3.000000  3.000000
     mean   0.693046  0.608697
     std    0.257070  0.522231
     min    0.396401  0.011814
     25%    0.614231  0.422315
     50%    0.832060  0.832817
     75%    0.841368  0.907138
     max    0.850676  0.981459
b    count  2.000000  2.000000
     mean   0.352287  0.482039
     std    0.343271  0.675147
     min    0.109558  0.004638
     25%    0.230922  0.243339
     50%    0.352287  0.482039
     75%    0.473651  0.720740
     max    0.595016  0.959441

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4. Summary

• By using the apply()method, we can call their own definition of the function, makes the code more clear structure, concise.
• Since the first argument definition, it represents the next "latitude" .