Run this code: df_students.reset_index.FIRST_NAME We get an error that begins as follows: 'function' object has no attribute ... This is another telltale ...
pandas.DataFrame.reset_index¶ DataFrame. reset_index (level = None, drop = False, inplace = False, col_level = 0, col_fill = '') [source] ¶ Reset the index, or a level of it. Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.
19.11.2021 · Thanks to answers so far (I’ve made comments there as I haven’t got those solutions to work–maybe I’m not understanding something). In the meantime, I’ve also come up with another approach, which I still suspect isn’t very Pythonic.
25.08.2021 · 2 Answers2. Show activity on this post. I don't think there is reset_drop in pandas, but if you want to reset the index you can use df.reset_index (drop=True). Show activity on this post. You're trying to call a pandas function on a numpy array. You have to call it on an instance of pandas.DataFrame.
20.05.2019 · Pandas 'function' object has no attribute 'to_excel' Ask Question Asked 2 years, 7 months ago. Active 2 years, 7 months ago. Viewed 5k times 0 I'm unable to get the 'to_excel' method to function correctly. My code is as follows: import pandas as pd file ...
I am trying to remove '$' and ',' from my income column and then apply on my original dataframe. so I created below function. However, it is giving me error ...
29.10.2019 · AttributeError: 'function' object has no attribute 'predict' 該当のソースコード. import numpy as np import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm import statsmodels.formula.api as smf . model = smf.glm('y + I(N-y)~ x + f',data=d,family=sm.families.Binomial()) result=model.fit . df_test_c = pd.DataFrame()
30.06.2018 · AttributeError: 'NoneType' object has no attribute 'replace' The solution that worked for me was related to using inplace=True and assigning the result of the line to df. So, here I had to either assign the result to df by writing df = df.drop... or by using inplace=True and not assigning the expression to df.