I have a dataframe and I'm using PySpark, when I'm showing the data, it not showing very well, like the next image:
enter image description here
How can I fix it? Thank You.
There's not a whole lot you can do. The issue is with line wrap. A common workaround is to use pandas
df.limit(5).toPandas().head()
If you're using a Jupyter Notebook, you can read more choices here: pyspark show dataframe as table with horizontal scroll in ipython notebook
Related
I have a large (vertically) pandas Dataframe that I would like to display as a nice table with (vertical) scrollbars in a jupyter notebook in vs code.
I have come across post that addresses the solution, but it is 5 years old, so was wondering if there is now a better method. Here is the post:
Pandas DataFrame Table Vertical Scrollbars
Right now I use the following to see all the data:
pd.set_option("display.max_rows", None)
But this shows all the rows which becomes problematic when, say >100 rows.
Just to be clear, i am looking for a scroll bar (as in the image):
I don't think there is a solution for plain Jupyter, but for the successor JupyterLab it's quite easy, not just for DataFrames but for all outputs.
It looks like this:
To enable this view you have to set pd.set_option("display.max_rows", None) and then you have to make a right-click on the blue column and choose Enable Scrolling for Outputs:
So, I am trying to make a Jupyter notebook that is slightly interactive in which I can change the number value of a variable, and then use that variable in a Markdown cell to display a Latex matrix like so:
And that cell displays this:
I don't know why that spanID thing is showing or how to get rid of it. I already have NBextensions installed and in that Markdown cell if I just type {{a_0}} then when I run that cell it just displays 1 like it should. But the moment I put it within the latex matrix, then I get the error. Any help with this is greatly appreciated.
Thanks!
The solution I applied for the problem is to create Latex objects in the code cells and displaying them in the markdown cells with Python Markdown extension of the Jupyter notebook extensions. For example, a code cell can have:
from IPython.display import Latex as lt
# Room_length [m]
l_rl = 30
l_rl_lt = lt(rf"$l_\mathrm{{rl}}={l_rl}\ \mathsf{{m}}$")
and the markdown cell can have:
The room has length size {{l_rl_lt}}.
Since variables in markdown cells are still not natively supported and it cannot be done without specific extensions (this is especially a problem in JupyterLab).
It is worth mentioning that you can also print the matrix directly from the code cell.
As an example, I will use function from my gist.
It basically looks like this:
I want to make my display tables bigger so users can see the tables better when that are used in conjunction with Jupyter RISE (slide shows).
How do I do that?
I don't need to show more columns, but rather I want the table to fill up the whole width of the Jupyter RISE slide.
Any idea on how to do that?
Thanks
If df is a pandas.DataFrame object.
You can do:
df.style.set_properties(**{'max-width': '200px', 'font-size': '15pt'})
For a "code presenting session", I would like to transform my Jupyter NoteBook to slides. It works pretty well actually but for the Dataframe display.
I have currently this display in my notebook:
and I get that on the Slide:
Why? Is it possible to get the same rendering on both notebook/slides?
Thanks
I have a simple csv file with ten columns!
When I set the following option in the notebook and print my csv file (which is in a pandas dataframe) it doesn't print all the columns from left to right, it prints the first two, the next two underneath and so on.
I used this option, why isn't it working?
pd.option_context("display.max_rows",1,"display.max_columns",100)
Even this doesn't seem to work:
pandas.set_option('display.max_columns', None)
I assume you want to display your data in the notebook than the following options work fine for me (IPython 2.3):
import pandas as pd
from IPython.display import display
data = pd.read_csv('yourdata.txt')
Either directly set the option
pd.options.display.max_columns = None
display(data)
Or, use the set_option method you showed actually works fine as well
pd.set_option('display.max_columns', None)
display(data)
If you don't want to set this options for the whole script use the context manager
with pd.option_context('display.max_columns', None):
display(data)
If this doesn't help, you might give a minimal example to reproduce your issue.
You can also display all the data by asking pandas to return HTML markup, and then having IPython render the HTML table.
import pandas as pd
from IPython.display import HTML
data = pd.read_csv('yourdata.csv')
HTML(data.to_html())
Using IPython 3.0.0 and Python 3.4, I found that display(data) as described by #Jakob will render as a table with up/down and left/right scroll bars, but the table is still wider than the cell and some columns are off-screen to the right. To see all the data, one must collapse the cell - which adds scroll bars. Consequently you have a scrolling box in a scrolling box, which is not ideal as you have to shift focus between the doubled-up scroll bars to navigate all the way through the data.
Using the HTML method, you render the enormous table as-is without any scroll bars. This cell can then be collapsed down to show only a single vertical and horizontal bar, which is more user-friendly.
The caveat to using HTML is the table takes longer to render. I was only using a ~150x50 matrix and the speed difference was noticeable, but not inconvenient. If you have an enormous table, don't use this method to display the entire thing at once. That said, if you do have an enormous table, rendering the whole thing at once is obviously going to be a bad idea however you try to do it.
I found this question as one of the first hits on Google. In jupyter lab,
pandas.set_option("display.max_columns", None)
Now seems to work fine - my example was 32 columns, it used to be truncated and is not any more.