chained-assignment

I've looked through a bunch of questions and answers related to this issue, but I'm still finding that I'm getting this copy of slice warning in places where I don't expect it. Also, it's cropping up in code that was running fine for me previously, leading me to wonder if some sort of update may be the culprit. For example, this is a set of code where all I'm doing is reading in an Excel file into a pandas DataFrame , and cutting down the set of columns included with the df[[]] syntax. izmir = pd.read_excel(filepath) izmir_lim = izmir[['Gender','Age','MC_OLD_M>=60','MC_OLD_F>=60','MC_OLD_M>18'

Is there an easy way to check whether two data frames are different copies or views of the same underlying data that doesn't involve manipulations? I'm trying to get a grip on when each is generated, and given how idiosyncratic the rules seem to be, I'd like an easy way to test. For example, I thought "id(df.values)" would be stable across views, but they don't seem to be: # Make two data frames that are views of same data. df = pd.DataFrame([[1,2,3,4],[5,6,7,8]], index = ['row1','row2'], columns = ['a','b','c','d']) df2 = df.iloc[0:2,:] # Demonstrate they are views: df.iloc[0,0] = 99 df2.iloc

This question already has an answer here: How to deal with SettingWithCopyWarning in Pandas? 13 answers I have a pandas dataframe: data. it has columns ["name", 'A', 'B'] What I want to do (and works) is: d2 = data[data['name'] == 'fred'] #This gives me multiple rows d2['A'] = 0 This will set the column A on the fred rows to 0. I've also done: indexes = d2.index data['A'][indexes] = 0 However, both give me the same warning: /Users/brianp/work/cyan/venv/lib/python2.7/site-packages/pandas/core/indexing.py:128: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a

This question already has an answer here: How to deal with SettingWithCopyWarning in Pandas? 13 answers I'd like to replace values in a Pandas DataFrame larger than an arbitrary number (100 in this case) with NaN (as values this large are indicative of a failed experiment). Previously I've used this to replace unwanted values: sve2_all[sve2_all[' Hgtot ng/l'] > 100] = np.nan However, I got the following error: -c:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead C:\Users\AppData\Local\Enthought

I try to delete some column and convert some value in column with df2.drop(df2.columns[[0, 1, 3]], axis=1, inplace=True) df2['date'] = df2['date'].map(lambda x: str(x)[1:]) df2['date'] = df2['date'].str.replace(':', ' ', 1) df2['date'] = pd.to_datetime(df2['date']) and to all this string I get df2.drop(df2.columns[[0, 1, 3]], axis=1, inplace=True) C:/Users/����� �����������/Desktop/projects/youtube_log/filter.py:11: 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 What is problem there? Your df2

At first, I tried writing some code that looked like this: import numpy as np import pandas as pd np.random.seed(2016) train = pd.DataFrame(np.random.choice([np.nan, 1, 2], size=(10, 3)), columns=['Age', 'SibSp', 'Parch']) complete = train.dropna() complete['AgeGt15'] = complete['Age'] > 15 After getting SettingWithCopyWarning, I tried using.loc: complete.loc[:, 'AgeGt15'] = complete['Age'] > 15 complete.loc[:, 'WithFamily'] = complete['SibSp'] + complete['Parch'] > 0 However, I still get the same warning. What gives? Note: As of pandas version 0.24, is_copy is deprecated and will be removed