How to use loc in pandas dataframe slicing?

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ce["Buy_Price"] = ce["Trade_price"][ce["Trade_type"] == "CE Buy"]

How to use loc in this line to avoid warning........."A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead"

pandas dataframe
2 Answers

If you want to give "Buy_Price" the value of "Trade_price" when [ce["Trade_type"] == "CE Buy"] you can use np.where:

import numpy as np
ce["Buy_Price"] = np.where(ce["Trade_type"] == "CE Buy", ce["Trade_price"], ce["Buy_Price"])

If you still want to use .loc:

ce.loc[ce['Trade_type']=='CE Buy','Buy_Price'] = ce['Trade_price']

Regarding your followup question about: ce = df[(df["Trade_type"] == "CE Buy") | (df["Trade_type"] == "CE Sell")]: This code should work ok. However, you can make it better like:

types = ['CE Sell', 'CE Buy']
ce = df[df['Trade_type'].isin(types)]
user_108363090

you should use .loc in this way to correct the warning.

ce["Buy_Price"] = ce.loc[:]["Trade_price"][ce["Trade_type"] == "CE Buy"]

in general:

df['new_column_name'] = df.loc[:][old_column_name][boolean condition]

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