This question already has answers here:
SettingWithCopyWarning even when using .loc[row_indexer,col_indexer] = value
(9 answers)
How to deal with SettingWithCopyWarning in Pandas
(20 answers)
Closed 12 months ago.
The line:
df["max_performance_factor_per_strat"] = df.groupby(["dsp_strategy_id"])[
"performance_factor"
].transform(max)
is throwing the following 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
How do I resolve this? What is causing it?
As already discussed in
How to deal with SettingWithCopyWarning in Pandas
it's a false positive warning that can be safely disabled as follows:
import pandas as pd
pd.options.mode.chained_assignment = None
Hope this helped you out.
Related
This question already has answers here:
How to deal with SettingWithCopyWarning in Pandas
(20 answers)
Closed 6 months ago.
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
HSN.rename(columns={'HSN Code':'HSN','Quantity':'Total Quantity','Total Amount':'Total Value',
d:\Vishal\Udemy\PD Data\Vishal\Python\GSTR1\GSTR1.py:130: SettingWithCopyWarning:
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
Before renaming make a copy of the dataframe and then rename:
HSN_copy = HSN.copy()
HSN_copy.rename(columns=......
This question already has answers here:
SettingWithCopyWarning even when using .loc[row_indexer,col_indexer] = value
(9 answers)
How to deal with SettingWithCopyWarning in Pandas
(20 answers)
Closed last year.
def nan_to_zeros(df, col):
new_col = f"nanreplace{col}"
df[new_col] = df[col].fillna('~')
return df
When running this function I get the
SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
warning and from what I can gather I am not using a chained assignment and adding inplace=True does nothing to silence the warning.
Any direction appreciated. Thanks!
This question already has answers here:
How to deal with SettingWithCopyWarning in Pandas
(20 answers)
SettingWithCopyWarning when using groupby and transform('first') for fillna
(1 answer)
Closed 1 year ago.
This post was edited and submitted for review 1 year ago and failed to reopen the post:
Original close reason(s) were not resolved
I need to use the following code:
raw_data.loc[(raw_data['PERMNO']==10006)&(raw_data['month']>=50)&(raw_data['month']<=100)]['resi']=raw_data['RET']-raw_data['ewretd']
that is based on the conditions to calculate column 'resi'.
But I keep getting warnings like
D:\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:
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
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
"""Entry point for launching an IPython kernel.
How to correct this?
Try adding df.copy:
raw_data = raw_data.copy()
raw_data.loc[(raw_data['PERMNO']==10006)&(raw_data['month']>=50)&(raw_data['month']<=100), 'resi'] = raw_data['RET'] - raw_data['ewretd']
This question already has answers here:
How to deal with SettingWithCopyWarning in Pandas
(20 answers)
Closed 1 year ago.
When I look at the answer to a similiar question as shown in this link: Pandas: adding column with the length of other column as value
I come across an issue where the solution its suggesting i.e
df['name_length'] = df['seller_name].str.len()
Throws the following warning
'''
SettingWithCopyWarning:
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
'''
My question is: How could this be done inorder to prevent this warning from occuring? As in this command I would like to add a new column to the original dataframe not create some sort of copy of a slice.
I took a sample data set to test this issue in Python 3.8
Sample data
here is the same code which you ran
df['name_length'] = df['seller_name'].str.len()
there was no error
This question already has answers here:
How to deal with SettingWithCopyWarning in Pandas
(20 answers)
Closed 5 years ago.
I add values to a dataframe entry by entry as followed:
refined_cme_quandl_list['typical_daily_volume']= np.nan
for index, row in refined_cme_quandl_list.iterrows():
refined_cme_quandl_list['typical_daily_volume'][index] = typical_volume[row['Quandl_download_symbol']]
I still get what i want, but i get this warning:
SettingWithCopyWarning: A value is trying to be set on a copy of a
slice from a DataFrame
See the caveats in the documentation:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Does it matter?
Yes, using boolean indexing directly to assign to slices is not recommended. Use df.loc instead:
refined_cme_quandl_list.loc[index, 'typical_daily_volume'] = \
typical_volume[row['Quandl_download_symbol']]
It is quite possible that future releases of pandas might disable this behaviour (direct indexing), so you don't want your code breaking in the future.