I have a pandas dataframe that I would like to pretty-print in full (it's ~90 rows) in a Jupyter notebook. I'd also like to display it without the index column, if possible. How can I do that?
For pretty-printing without an index, I think the right approach is to call the display method for HTML (which is what jupyter does under the hood):
from IPython.display import HTML
HTML(df.to_html(index=False))
(Credit to Display pandas dataframe without index)
As others have suggested you can use pd.display_max_rows() for the row count limitation.
In pandas you can use this
pd.set_option("display.max_rows", None, "display.max_columns", None)
please use this.
Without index use additionally.
df.to_string(index=False)
Related
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Hey everyone I am used to work in excel but recently after getting a dataset of about 500k rows that need to be worked in the same worksheet I have huge capacity issues and I was advised to try and transition any function to a python environment. So this excel function "=IF(COUNTIF($J$2:J3,J3)>1,0,1)"~J is the column of the Asset ID~ goes to each cell and if it has previous encountered it in the cells above it returns 0 and if it is unique it returns 1.
How that would be possible in a python environment if I load my table as a DataFrame?
Thanks in advance
You can use pandas to achieve this very easily:
import pandas as pd # import pandas
df = pd.read_excel('your_file.xlsx') # use appropriate function according to your file type
df['Unique'] = ~df[Asset_Id].duplicated().astype(int) # places 1 where it is not encountered before, 0 elsewhere
I try to use PySpark with jupyter notebook. But when I want to see (a part of) the dataframe,
...(some columns are even not shown).
I would like to have a display
.
Any idea how to do it?
Your dataframe is semicolon separated.
Pass that as a separator
df = spark.read.csv(path,sep=';')
I rendered a Pandas Dataframe to a webpage through Jinja but noticed the number column is left aligned.
When I tried applying the code below on the particular column to align right and loaded the webpage.
df = df.style.set_properties(subset=["col1", "col2"], **{'text-align': 'right'})
It gives an error on the browser page. Funny enough it works perfectly when tried on Jupyter Notebook
TypeError: 'Styler' object is not subscriptable
What I want is the number column to align right.
Anyone has a better solution.
I couldn't get a Pandas or Jinja solution that worked. However I stumbled on a this and that solved the whole issue.
It was a CSS trick. I simply had to identify the specific column and applied the code below in my Style.css file.
tbody>tr>:nth-child(5){
text-align:right;
}
The '5' being the column number.
Credit to Charles Riebeling
I believe this will be of help to someone.
This is what the dataframe looks like before exporting
After that it becomes
Rounding down is not what I want here; I want the text in txt.file look like what it is shown in the console. So how can I fix this? Any simple solutions?
Did you try writing directly from your Pandas dataframe instead of going through Numpy?
Try DF.to_csv(‘output.txt’, sep=‘\t’, float_format=‘%g’)
For more details see pandas.DataFrame.to_csv
Wondering if someone can help me here. When I take a regular python list containing strings, and check to see if a pandas series (pla.id) has a value that matches a value in that list. It works.
Which is great and all but I wonder how it's able to compare strings to ints... is there documentation somewhere that states that it will convert under the hood before comparing those values??
I wasn't able to find anything on the .isin() page of pandas documentation..
Also super interesting is that when I try pandas indexing it fails due to a type comparison.
So my two questions:
Does pandas.series.isin(some_list_of_strings) method automatically convert the values in the series (which are int values) to strings before doing a value comparison?
If so, why doesn't pandas indexing i.e. (df[df.series == 'some value']) not do the same thing? What is the thinking behind this? If I wanted to accomplish the same thing I would have to do df[df.series.astype(str) == ''] or df[df.series.astype(str).isin(some_list_of_strings)] to access those values in the df that match
After some digging I think this might be due to the pandas object datatype? but I have no understanding of why this works. Also this doesn't explain why the below works... since it is a int dtype
Thanks in advance!