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This article introduces the knowledge of "how to use the index alignment method of Pandas in Python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

one。 Indexed objects support set operations: Union, crossover, difference, symmetry difference.

Demo1:

Import pandas as pd import numpy as np college = pd.read_csv ('data/college.csv') columns = college.columnsc1 = columns [: 4] c2 = columns [2:5] print (c1.union (c2)) print (C1 | c2)

Demo2:

Import pandas as pd import numpy as np college = pd.read_csv ('data/college.csv') columns = college.columnsc1 = columns [: 4] c2 = columns [2:5] print ("C1:", C1) print ("c2:", c2) print (c1.symmetric_difference (c2)) print (C1 ^ c2)

two。 Generate new data with copy ()

An is B: indicates that they both point to the same object. This means that if one is modified, the other will change as well.

Demo1:

three。 Unequal index (difference method of index)

Demo1:

Use difference to find out which tabs are in baseball_14 but not in baseball_15 or baseball_ 16

Import pandas as pd import numpy as np baseball_14 = pd.read_csv ('data/baseball14.csv', index_col='playerID') baseball_15 = pd.read_csv (' data/baseball15.csv', index_col='playerID') baseball_16 = pd.read_csv ('data/baseball16.csv', index_col='playerID') print (baseball_14.index.difference (baseball_15.index)) print (baseball_14.index.difference (baseball_16.index))

four。 Use fill_value to avoid missing values in arithmetic operations

Demo1:

Import pandas as pd import numpy as np baseball_14 = pd.read_csv ('data/baseball14.csv', index_col='playerID') baseball_15 = pd.read_csv (' data/baseball15.csv') Index_col='playerID') # H column: hits_14 = baseball_14 ['H'] hits_15 = baseball_15 ['H'] print (hits_14.head ()) print (hits_15.head ()) print (hits_14.head () + hits_15.head ())

The following four pieces of data are recorded, but because they do not exist in two tables at the same time, the sum will produce NaN, which requires the use of fill_value

Demo2:

Import pandas as pd import numpy as np baseball_14 = pd.read_csv ('data/baseball14.csv', index_col='playerID') baseball_15 = pd.read_csv (' data/baseball15.csv', index_col='playerID') baseball_16 = pd.read_csv ('data/baseball16.csv') Index_col='playerID') # H column: hits_14 = baseball_14 ['H'] hits_15 = baseball_15 ['H'] hits_16 = baseball_16 ['H'] print (hits_14.head (). Add (hits_15.head (), fill_value=0))

* if an element is a missing value in both Series, even if fill_value is used, the result is still missing.

five。 Append columns from different DataFrame

Demo:

Import pandas as pd import numpy as np employee = pd.read_csv ('data/employee.csv') D1 = employee [[' DEPARTMENT', 'BASE_SALARY']] print ("before sorting:") print (d1.head ()) # within each department Sort BASE_SALARY D2 = d1.sort_values (['DEPARTMENT',' BASE_SALARY'], ascending = [True] False]) print ("sorted:") print (d2.head ()) # use the drop_duplicates method to retain the first line of each department d3 = d2.drop_duplicates (subset = 'DEPARTMENT') print (' deduplicated:') print (d3.head ()) # use DEPARTMENT as the row index d3 = d3.set_index ('DEPARTMENT') employee = employee.set_index (' DEPARTMENT') # add a column to employee's DataFrame The corresponding missing item is the missing value # storing the maximum wage of each Department employee ['MAX_SALARY'] = d3 [' BASE_SALARY'] pd.options.display.max_columns = 3print ('merged:') print (employee.head ()) # check with query to see if there is any # output with BASE_SALARY greater than MAX_DEPT_SALARY should be 0print ('query result:') print (employee.query ('BASE_SALARY > MAX_SALARY'))

Employee ['MAX_SALARY'] = d3 [' BASE_SALARY']

The condition that this statement can be executed successfully is that there is no duplicate index in d3, that is, drop_duplicates has been executed

Running result:

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