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Panda pivot table margins only on row [closed]
(1 answer)
Pandas: add crosstab totals
(3 answers)
Pandas dataframe total row
(13 answers)
Closed 6 months ago.
This post was edited and submitted for review 6 months ago and failed to reopen the post:
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I have the following code for a table:
n_area = pd.crosstab(index=airbnb["neighbourhood"], columns=airbnb["neighbourhood_group"])
and I would like to add a row of totals (count) in the end of each column with a single function, completing what I have already written
this all comes from the dataset here
https://www.kaggle.com/code/dgomonov/data-exploration-on-nyc-airbnb/data
A screenshot of the data is as follows
Related
This question already has answers here:
Calculate Time Difference Between Two Pandas Columns in Hours and Minutes
(4 answers)
How to calculate time difference between two pandas column [duplicate]
(2 answers)
Closed 1 year ago.
This post was edited and submitted for review 1 year ago and failed to reopen the post:
Original close reason(s) were not resolved
Hi I am trying to calculate time differences for certain tasks in some data I am working on. I have a csv file with a bunch of data, the relevant columns look like below:
ID
Start Date
End Date
123456
10/08/2021 02:00:05 AM
10/11/2021 01:00:15 AM
324524
10/11/2021 01:00:15 AM
10/08/2021 02:00:05 AM
My goal is to create a new file with the row ID, the start date, end date, and the time difference in hours.
So far I have used pandas.to_datetime to change the format of the start date and the end date. Now I am wondering how I can calculate the difference between the two times i.e. (end date - start date) in hours and create a new column in the dataframe to store it in.
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How to deal with SettingWithCopyWarning in Pandas
(20 answers)
SettingWithCopyWarning when using groupby and transform('first') for fillna
(1 answer)
Closed 1 year ago.
This post was edited and submitted for review 1 year ago and failed to reopen the post:
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I need to use the following code:
raw_data.loc[(raw_data['PERMNO']==10006)&(raw_data['month']>=50)&(raw_data['month']<=100)]['resi']=raw_data['RET']-raw_data['ewretd']
that is based on the conditions to calculate column 'resi'.
But I keep getting warnings like
D:\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
"""Entry point for launching an IPython kernel.
How to correct this?
Try adding df.copy:
raw_data = raw_data.copy()
raw_data.loc[(raw_data['PERMNO']==10006)&(raw_data['month']>=50)&(raw_data['month']<=100), 'resi'] = raw_data['RET'] - raw_data['ewretd']
This question already has answers here:
How do I expand the output display to see more columns of a Pandas DataFrame?
(22 answers)
Closed 1 year ago.
I am a complete novice when it comes to Python so this might be badly explained.
I have a pandas dataframe with 2485 entries for years from 1960-2020. I want to know how many entries there are for each year, which I can easily get with the .value_counts() method. My issue is that when I print this, the output only shows me the top 5 and bottom 5 entries, rather than the number for every year. Is there a way to display all the value counts for all the years in the DataFrame?
Use pd.set_options and set display.max_rows to None:
>>> pd.set_option("display.max_rows", None)
Now you can display all rows of your dataframe.
Options and settings
pandas.set_option
If suppose the name of dataframe is 'df' then use
counts = df.year.value_counts()
counts.to_csv('name.csv',index=false)
As our terminal can't display entire columns they just display the top and bottom by collapsing the remaining values so try saving in a csv and see the records
This question already has answers here:
Using a loop in Python to name variables [duplicate]
(5 answers)
Append multiple pandas data frames at once
(5 answers)
Closed 5 years ago.
I have pandas data frame numbered from x1,x2....x100 with same columns.
I want to append them all using a for loop. How can i do that?
I know how to append two dataframe but how to do it for 100 of them. The main problem here is how can i have a dynamic variable name.
I want to append the data frames not concat.
x=x1.append(x2)
x=x.append(x3)
and so on.
I want to this in a loop.
This question already has answers here:
Pandas column creation
(3 answers)
Accessing Pandas column using squared brackets vs using a dot (like an attribute)
(5 answers)
pandas dataframe where clause with dot versus brackets column selection
(1 answer)
Closed 5 years ago.
I just thought I added a column to a pandas dataframe with
df.NewColumn = np.log1p(df.ExistingColumn)
but when I looked it wasn't there! No error was raised either.. I executed it many times not believing what I was seeing but yeah, it wasn't there. So then did this:
df['NewColumn'] = np.log1p(df.ExistingColumn)
and now the new column was there.
Does anyone know the reason for this confusing behaviour? I thought those two ways of operating on a column were equivalent..