Given the following DataFrame of pandas in Python: Displays the switching on and off of 3 light bulbs at different times using date and time objects. I want to add a new column, called cost_days. This column will include only for rows where the variable using_time is different from NaT. Information on how many times the light bulb has been
Tag: dataframe
python_ hh:mm:ss.000 in excel converts to dataframe by xlwings has problem
When I build exe file because of DRM i can’t use pd.read_excel or pd.ExcelFile for open excel files. So I try to use xlwings for open the DRM excel file. But time data converts to something strange data…by xlwings. I don’t know what’s it and how to fix. -here is time data- result : Answer In Excel, Time is stored
I want to add date range where value is True in pandas
Dt 1/2/21 2/2/21 3/2/21 4/2/21 5/2/21 6/2/21 7/2/21 Attendance(Expected output in python) san TRUE TRUE TRUE TRUE TRUE TRUE TRUE 1/2/21 – 7/2/21 don TRUE TRUE FALSE TRUE TRUE TRUE TRUE 1/2/21 -2/2/21,4/2/21-7/2/21 sam FALSE TRUE TRUE FALSE TRUE TRUE TRUE 2/2/21 – 3/2/21,5/2/21-7/2/21 den FALSE FALSE TRUE FALSE TRUE TRUE FALSE 3/2/21,5/2/21 – 6/2/21 I want to add Attendance
extract key and values from nested jsons and put in a DataFrame
I have a json object that looks like this: Is it possible to extract all dates (key) along with their respective “revenue” values and put them into a dataframe that looks likethis: Answer How about something like:
panda dataframe extracting values
I have a dataframe called “nums” and am trying to find the value of the column “angle” by specifying the values of other columns like this: When I do so, I do not get a singular number and cannot do calculations on them. What am I doing wrong? nums Answer First of all, in general you should use .loc rather
Dask Df convert All Dtype using dictionary
Is there an easy equivalent way to convert all columns in a dask df(converted from a pandas df) using a dictionary. I have a dictionary as follows: and would like to convert the pandas|dask df dtypes all at once to the suggested dtypes in the dictionary. Answer Not sure if I understand the question correctly, but the conversion of dtypes
Pandas dataframe: select list items in a column, then transform string on the items
One of the columns I’m importing into my dataframe is structured as a list. I need to pick out certain values from said list, transform the value and add it to one of two new columns in the dataframe. Before: Name Listed_Items Tom [“dr_md_coca_cola”, “dr_od_water”, “potatoes”, “grass”, “ot_other_stuff”] Steve [“dr_od_orange_juice”, “potatoes”, “grass”, “ot_other_stuff”, “dr_md_pepsi”] Phil [“dr_md_dr_pepper”, “potatoes”, “grass”, “dr_od_coffee”,”ot_other_stuff”] From
Aggregating Pandas DF – Losing Data
I’m trying to aggregate a pandas df in a way an excel pivot table would. I have one quantitative variable called “Count”. I would like the same qualitative variables to combine and the “Count” data to sum. However, when I am trying to do this with the below code, I see that I am somehow losing data. Any idea why
Parsing a Pandas Dataframe
I have a dataframe like this; Looking like this in Jupyter notebook output; I want to parse this table so that table name repeats with each field name and column counts remain the same such as the output dataframe will look like; I tried this code from a stackoverfow solution; But it did not work. In the solution; Unlist multiple
return index of matching value over columns in dataframe
my input: I trying get over df all range where matching value is true. My code: What I get:* [2, 3, 4, 5] So its correct, but how to get all index over each column in df? In other word, I dont want input manually name of column to get index matching value, but get ouptut per column. So what