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Create a Pipeline and remove a row from an already created DataFrame - Python Pandas
Use the ValDrop() method of pdpipe library to remove a row from an already create Pandas DataFrame. At first, import the required pdpipe and pandas libraries with their respective aliases −
import pdpipe as pdp import pandas as pd
Let us create a DataFrame. Here, we have two columns −
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 150, 110, 80, 110, 90]
}
)Now, remove a row using valdDrop() method −
dataFrame = pdp.ValDrop(['Jaguar'],'Car').apply(dataFrame)
Example
Following is the complete code −
import pdpipe as pdp
import pandas as pd
# function to check for excess units
def demo(x):
if x >= 100:
return "OverStock"
else:
return "UnderStock"
# Create DataFrame
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 150, 110, 80, 110, 90]
}
)
print("DataFrame ...\n",dataFrame)
# adding a new column "Stock" and its values based on an already created column "Units"
dataFrame['Stock'] = dataFrame['Units'].apply(demo)
print("\n DataFrame with a new column...\n",dataFrame)
# removing a row with pdp
dataFrame = pdp.ValDrop(['Jaguar'],'Car').apply(dataFrame)
print("\n DataFrame after removing a row...\n",dataFrame)Output
This will produce the following output −
DataFrame ... Car Units 0 BMW 100 1 Lexus 150 2 Audi 110 3 Mustang 80 4 Bentley 110 5 Jaguar 90 DataFrame with a new column... Car Units Stock 0 BMW 100 OverStock 1 Lexus 150 OverStock 2 Audi 110 OverStock 3 Mustang 80 UnderStock 4 Bentley 110 OverStock 5 Jaguar 90 UnderStock DataFrame after removing a value... Car Units Stock 0 BMW 100 OverStock 1 Lexus 150 OverStock 2 Audi 110 OverStock 3 Mustang 80 UnderStock 4 Bentley 110 OverStock
Updated on: 01-Oct-2021
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