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Questions - pandas

Pandas read_excel with formulas and get values

So, I had some code running that opened a series of excel files and converted to dataframes using pd.read_excel, no issues. Just today, the files are now all displaying NaN instead of values. The ex...
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John Taylor

excel

pandas

excel-formula

Votes: 0

Answers: 1

Latest Answer

Well, after hours of searching, this code, for some reason that is opaque to me, enabled me to use pd.read_excel() and read the values instead of just all NaN values from cells with formulas. import x...
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John Taylor

How to download a csv file in Python

I am trying to download a csv file from the url https://qubeshub.org/publications/1220/supportingdocs/1#supportingdocs . the file is Elephant Morphometrics and Tusk Size-originaldata-3861.csv I have t...
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Mossman

python

pandas

csv

Votes: 0

Answers: 3

Latest Answer

Try: import requests url = "https://qubeshub.org/publications/1220/serve/1/3861?el=1&download=1" r = requests.get(url) filename = r.headers["Content-Disposition"].split('&quo...
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Andrej Kesely

Remove duplicate data based on the same unix time

multiple data on the same date. I am trying to remove the multiple data and have the data aligned based on the unix time given, I tried using remove duplicate but its not working time x...
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user191762

python

pandas

dataframe

Votes: 0

Answers: 1

Latest Answer

df[ ~df['time'].duplicated() ] (with ~) works for me. I use io only to simulate file - so everyone can copy it. data = ''' time x y 0 1648598400000 233 6758 1 1648598400000 234 ...
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furas

How to set list of values present in pandas dataframe, based on groupby key?

I am using pandas.dataframe in python code to find total pending_count & list of ticket_ids for a priority (based on priority) as below. df = pd.DataFrame({'priority': priority, ...
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Tanuj Kathuria

python

pandas

dataframe

pandas-groupby

psycopg2

Votes: 0

Answers: 1

Latest Answer

I believe you can try using groupby with .agg Remember to import numpy as np, instead of sum(), we use np.sum for priority, pending_count in df.groupby(['priority']).agg({'pending_count': np.sum, &quo...
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