1. Create a database with missing values:
import pandas as pd
import numpy as np
df = ((5, 3), index = list('abcde'), columns = ['one', 'two', 'three']) # Randomly generate 5 rows and 3 columns of data
[1, :-1] = # Define specified data as missing
[1:-1, 2] =
print('\ndf1') # Output df1, then newline
print(df)
View data content:
2. Normally, rows are deleted with axis = 0, and columns are deleted with axis = 1, which is usually not done, as that would delete a variable.
print('\ndrop row')
print((axis = 0))
Result after deletion:
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