Sometimes you may want to set a data name by saving or minimizing other columns while using Pandas data.
We can discard columns in a few ways. We will use the Pandas drop() function to learn to drop more columns and get a minor Pandas data name.
For our toy data, we also implied NumPy data. Using a random NumPy module with index names and columns, we create data for Pandas data frame.
Place Multiple Columns using Pandas drop() with axis = 1
We can manage the Pandas drop() function from the data frame to drop multiple columns. Pandas drop() is flexible and can use data lists to drop as well.
To use the Pandas drop() function to drop columns, several columns that need to be dropped as a list are also provided by Pandas. In addition, to tell the drop() function.
We also need to designate the axis = 1 argument that we are dropping columns. The process drops lines of the data list when the axis = 0 drops ().
Place More Columns using Pandas drop() in columns
Without using the axis = 1 argument, we can also use the Pandas drop() function. However, we need to designate the “columns” to discuss the list of column names to be eliminated.
For example, we need to designate “columns = [‘A’, ‘B’]” as a function of the drop() function to discard columns A and B to see similar results as before, and the two columns will drop.
To dump many local columns, we can also use the Pandas drop() function. This changes the actual name of the data. We are dropping columns without creating a new data name.” in place = True” is designated.
Another way to lower specific columns is to select the remaining columns using the Pandas [[]] function. For example, by discarding columns A and B, in this instance, C and D, we can choose the remaining columns, And the same results are shown.
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5']
}
# Convert the dictionary into DataFrame
df = pd.dataFrame(data)
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