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The apply (func [, args [, kwargs]]) function is used to call a function indirectly when its arguments already exist in a tuple or dictionary. An args is a tuple that contains positional arguments to be supplied to a function. If args is omitted, none of the parameters will be passed, and kwargs is a dictionary that contains keyword arguments. To put it simply, the return value of apply () is the return value of func (). The element parameters of apply () are ordered, and the order of the elements must be the same as the order of the parameters in the form of func (). The difference from map is that the former is for column and the latter is for elements.

Lambda is an anonymous function, that is, it no longer uses the form of def, which can simplify the script and make the structure non-redundant and concise.

A = lambda x: X + 1 a (10) 11

The combination of the two can do a lot of things. For example, many functions of split are not available in series, but index can do it.

For example, if you have a string of data as follows, which should be divided into totals, correct numbers, and correct rates, you can do so.

96% (1368608 Compact 1412722)

97% (1389916 Universe 1427922)

97% (1338695 Compact 1373803)

96% (1691941Compact 1745196)

95% (1878802 Compact 1971608)

97% (944218max 845)

96% (1294939amp 1336576)

Mr. import pandas as pd# becomes a dataframed = {"col1": ["96% (1368608max 1412722)", "97% (1389916max 1427922)", "97% (1338695 ash 1373803)", "96% (1691941max 1745196)", "95% (1878802max 1971608)", "97% (944218max 845)", "96% (1294939ama 1336576)"} df1 = pd.DataFrame (d) # Total recognition rate in the original text Using apply + anonymous function # lambda function means to select the sequence value of x For example, the x [6:9] # index function means to convert the current character position to the number of digits in it. #-1 is the last bit df1 ['correct number'] = df1.iloc [:, 0] .apply (lambda x: X [x.index ('() + 1: x.index ('/')]) df1 ['Total'] = df1.iloc [: 0] .apply (lambda x: X [x.index ('/') + 1:-1]) df1 ['accuracy'] = df1.iloc [:, 0] .apply (lambda x: X [: x.index ('()]) df1

Example 2

From a set of dataframe data, including three columns of numerical meteorological elements, the human comfort index is calculated by formula.

Applied to the calculation formula of human comfort index:

Import pandas as pdimport numpy as npimport mathpath='D:\\ data\\ 57582.csv' # File path data=pd.read_csv (path,index_col=0,encoding='gbk') # use gbk decoding when reading data in Chinese # define comfort index formula function Results retain 1 decimal place def get_CHB (Tgrame RHMague S): return round (1.8 percent TMEC 0.55 * (1.8*T-26) * (1-RH/100)-3.2*math.sqrt (S) + 32) # add a column of CHB and calculate the data and assign the value data ['comfort index'] = data.apply (lambda x:get_CHB (x ['average temperature'], x ['average relative humidity'], x ['2m wind speed]) Axis=1) # print result print (data) # Save result data.to_csv ('D:\\ CHB.csv',encoding='gbk')

The combination of apply and lambda is used in the code. The parameter x passed in is the entire data data, and the parameters introduced in the function are x ['average temperature'], x ['average relative humidity'], x ['2m wind speed'], corresponding to the custom function get_CHB. Finally, you need to use axis=1 to specify whether to operate on the column.

The result is shown in the figure:

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