I wanted to create variables in python based on the column names of my dataframe. Not sure if this is possible as I am quite new to Python. Lets say my df looks
I wanted to create variables in python based on the column names of my dataframe. Not sure if this is possible as I am quite new to Python. Lets say my df looks
While using: with open("data_file.pickle", "rb") as pfile: raw_data = pickle.load(pfile) I get the error: AttributeError: Can't get attribute '_unpickle
I am reading values from text file and and trying to find index of sub strings like below df=pd.read_csv('break_sent.txt', index_col=False,encoding='utf-8',del
I have diferent dataframes and need to merge them together based on the date column. If I only had two dataframes, I could use df1.merge(df2, on='date'), to do
I have 2 columns containing date and time(hr,min,seconds:milliseconds) How do I remove the milliseconds from only one of the column? Name MinTime
I've looked at the Sklearn stratified sampling docs as well as the pandas docs and also Stratified samples from Pandas and sklearn stratified sampling based on
I am trying to convert a float pandas column with nans to int format, using apply. I would like to use something like this: df.col = df.col.apply(to_integer) w
I have the following panda Series: print(df.head()) Country Energy Supply Energy Supply per Capita % Renewable 0 Afghanistan 3.210000e+08
I have the following dataframe: df = {'id': [1,2,3,4], '1': ['Green', 'Green', 'Green', 'Green'], '2': ['34','67', 'Blue', '77'], '3': ['Blue', '45', '9
I'm trying to find out error in a word similarity calculation. def word_similarity_error_analysis(eval_df): eval_df['distance_rank'] = _normalized_ranking(e
I'm trying to take one dataframe and create another, with all possible combinations of the columns and the difference between the corresponding values, i.e on 1
I have a dataset where all values in column B are the same. It looks like this: A B 0 Marble Hill Pizza Place 1 Ch
When I try to apply the values_count() method to series within a function, I am told that 'Series' object has no attribute 'values_counts'. def replace_1_occ_f
I've recently started to use Google Cloud Platform and I run my python scripts in Cloud Shell within Linux environment. By running one of the scripts that is u
I'm having trouble figuring out how to add attributes to nodes in my network from columns in my dataframe. I have provided an example of my dataframe below, t
I have a dataframe with 3 columns: file = glob.glob('InputFile.csv') for i in file: df = pd.read_csv(i) df['Date'] = pd.to_datetime(df['Date']) pri
I have a dataframe with 3 columns: file = glob.glob('InputFile.csv') for i in file: df = pd.read_csv(i) df['Date'] = pd.to_datetime(df['Date']) pri
I want to be able to convert a wide form dataframe to long form, but while doing that I also want to add columns that specify the column names where the values
I have time from epochs timestamps I use data.Time_req = pd.to_datetime(data.Time_req) But I get UTC time, I need +5:30 from the given time. How do I tell panda