This question already has answers here:
pandas read_csv remove blank rows
(4 answers)
Closed 1 year ago.
I’ve read a file into a dataframe, and every second row is n/a. How do I remove the offending blank rows?
I am assuming there are many ways to do this. But I just use iloc
df = df.iloc[::2,:]
Try it and let me know if it worked for you.
Related
This question already has answers here:
How to avoid pandas creating an index in a saved csv
(6 answers)
Closed 5 months ago.
I'm using the pandas split function to create new columns from an existing one. All of that works fine and I get my expected columns created. The issue is that it is creating an additional column in the exported csv file. So, it says there are 3 columns when there are actually 4.
I've tried various functions to drop that column, but it isn't recognized as part of the data frame so it can't be successfully removed.
Hopefully someone has had this issue and can offer a possible solution.
[example of the csv data frame output with the unnecessary column added]
The column A doesn't come from split but it's the index of your actual dataframe by default. You can change that by setting index=False in df.to_csv:
df.to_csv('{PATH}.csv', index=False)
This question already has answers here:
How do I melt a pandas dataframe?
(3 answers)
Closed 7 months ago.
I have a table in csv that looks like this:
I want to tranpose it to look like this, where the columns are nmow rows of a new column called ACCOUNTLABEL, and the values are in a corresponding column called VALUE:
Any help? thanks!
You might want to look at pandas.melt function : https://pandas.pydata.org/docs/reference/api/pandas.melt.html
I wouldn't call that a 'transposition' but 'un-pivoting' a table.
Edit: I just noticed that your question has nothing to do with transposing a DataFrame, but I will leave this here, in case it helps.
Use df.T for this. It uses the linked method.
I didn't downvote your question, but someone did because the provided link is the first search result if you google 'transpose pandas dataframe'.
This question already has answers here:
Pandas read in table without headers
(5 answers)
Closed 1 year ago.
Okay so I was reading up a text file and using .read_csv() and ended up with this dataframe:
But the problem is that, the im feeling rather rotten... text is ended up being as a column rather than a dataframe feature, and when I try to rename the column I just end up losing the feature all together, and skipping onto the 2nd value in the dataframe:
EDIT:
This is how I read in the text file.
Any answers, comments are heartfully accepted.
The final solution would be (respectfully concluded by #luigigi)
pd.read_csv("emotions.txt", sep=";", header=None)
Thanks!
You can pre-defined the columns name with the code.
df = pd.read_csv('emotions.txt', sep =';', names=['TEXT','EMOTION'], header=None)
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:
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..