Using the following answer from n1k31t4 in: https://datascience.stackexchange.com/questions/37717/imputation-missing-values-other-than-using-mean-median-in-python I was able to solve my problem. df[col]=df.groupby([‘X’, ‘Y’])[col].transform(lambda x: x.median() if (np.abs(x)>3).any() else x) solved How can I impute values to outlier cells based on groups? [closed]
There are several ways to transform this variable. In the picture I see, that there are not only bins, but also value ’55+’, it needs to be considered. 1) One liner: df[‘age’].apply(lambda x: np.mean([int(x.split(‘-‘)[0]), int(x.split(‘-‘)[1])]) if ‘+’ not in x else x[:-1]) It checks whether the value contains ‘+’ (like 55+), if yes than the … Read more
Maybe I’m missing something but doesn’t this work for you? agg = df.groupby(‘number_of_genres’).agg(‘sum’).T agg[‘totals’] = agg.sum(axis=1) Edit: Solution via pivot_table agg = df.pivot_table(columns=”number_of_genres”, aggfunc=”sum”) agg[‘total’] = agg.sum(axis=1) 2 solved Pivoting a One-Hot-Encode Dataframe
Can use np.roll to find the last index of three consecutive values. import numpy as np idx = ((l == np.roll(l, 1)) & (l == np.roll(l, 2))).cumsum().argmax() l[idx:] #[1, 2, 4, 1, 3, 5, 2, 2, 6, 4, 1, 6, 1, 1, 3, 5, 2, 2, 6, 4, 1, 6, 2, 7, 2, 6, 1, … Read more
Ok, let’s assume we have a dataframe data and list negative_words like this: data = pd.DataFrame({ ‘Tweets’ : [‘This is bad’, ‘This is terrible’, ‘This is good’, ‘This is great’], }) negative_words = [‘bad’, ‘terrible’] We can then do something like: 1) We can use a lambda function with any: # create lambda with any: … Read more
How can I start writing a program in python where it reads an excel file with few records and generate more record for testing purpose solved How can I start writing a program in python where it reads an excel file with few records and generate more record for testing purpose
There’s nothing wrong with your code. However recent stock data is a Premium product from Quandl and I presume you are just on the free subscription, hence your dataframe comes back empty. If you change the dates to 2017, you will get some results but that’s as far as it goes on the free subscription … Read more
I think you need Series.cumsum with select last row (total row) by DataFrame.iloc: df = pd.DataFrame({ ‘B’:[4,5,4], ‘C’:[7,8,9], ‘D’:[1,3,5], ‘E’:[5,3,6], }) df.loc[‘sum’] = df.sum() df.loc[‘cumsum’] = df.iloc[-1].cumsum() #if need only cumsum row #df.loc[‘cumsum’] = df.sum().cumsum() print (df) B C D E 0 4 7 1 5 1 5 8 3 3 2 4 9 5 … Read more
Because you change the question here is the updated answer: See comments in code import pandas as pd import matplotlib.pyplot as plt from matplotlib import style style.use(‘ggplot’) %matplotlib inline # read your dataframe and sort df = pd.read_clipboard() df.drop(columns=[‘length’], inplace=True) df.rename(columns={‘Text.1’: ‘Text length’}, inplace=True) df.sort_values([‘Text’, ‘Tag’, ‘Time’], inplace=True) x = list(df[‘Time’]) # set x axis … Read more
It seems that in the documentation of xlwings, it is necessary to have an empty row and column at the bottom and to the right. if not it will overwrite it http://docs.xlwings.org/en/stable/api.html#xlwings.xlwings.ret solved xlwings udf function erase next cell on excel
This looks like an excel datetime format. This is called a serial date. To convert from that serial date you can do this: data[‘Date’].apply(lambda x: datetime.fromtimestamp( (x – 25569) *86400.0)) Which outputs: >>> data[‘Date’].apply(lambda x: datetime.fromtimestamp( (x – 25569) *86400.0)) 0 2013-02-25 10:00:00.288 1 2013-02-26 10:00:00.288 2 2013-02-27 10:00:00.288 3 2013-02-28 10:00:00.288 To assign it … Read more
Please never use reserved words like list, type, id… as variables because masking built-in functions. If later in code use list e.g. list = data[‘FirstNames’].tolist() #another solution for converting to list list1 = list(data[‘SecondNames’]) get very weird errors and debug is very complicated. So need: L = data[‘FirstNames’].tolist() Or: L = list(data[‘FirstNames’]) Also can check … Read more
Commented for explanation of approach. Have found two addresses where ID from sheet2 comes back onto sheet1 import io sheeta = pd.read_csv(io.StringIO(“”” house_number street suburb 0 43 Smith Street Frewville 1 45 Smith Street Frewville 2 47 Smith Street Frewville 3 49 Smith Street Frewville 4 51 Smith Street Frewville 5 53 Smith Street Frewville … Read more
Press Ctrl + C to send a terminating signal, this will close the Bokeh server so you can reuse the terminal. (This isn’t just for Bokeh, it also works for other applications!) 1 solved How to reuse the terminal after I ran a Bokeh app in the server
As MrSmily2019 said you will want to use https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html. It does more than just CSV, it can do text. Additionally you can turn text files into csv. You file seems to be “TAB” delimited (how it know to separate) instead of comma. You can adjust the settings so it knows to do it on the … Read more