How to See All Rows of a Data Frame in a Jupyter Notebook with an R Kernel

How to see all rows of a data frame in a Jupyter notebook with an R kernel?

Thomas Kluyver says:

I think the options you want are repr.matrix.max.rows and
repr.matrix.max.cols

i.e. run

options(repr.matrix.max.rows=600, repr.matrix.max.cols=200)

The defaults are 60 and 20.

How to print an entire data frame in R on a Jupyter Notebook?

I think the options you want are repr.matrix.max.rows and repr.matrix.max.cols.

Try to run this:

options(repr.matrix.max.rows=600, repr.matrix.max.cols=200)

The defaults are 60 and 20.

Link: https://github.com/IRkernel/IRkernel/issues/470

Display all dataframe columns in a Jupyter Python Notebook

Try the display max_columns setting as follows:

import pandas as pd
from IPython.display import display

df = pd.read_csv("some_data.csv")
pd.options.display.max_columns = None
display(df)

Or

pd.set_option('display.max_columns', None)

Pandas 0.11.0 backwards

This is deprecated but in versions of Pandas older than 0.11.0 the max_columns setting is specified as follows:

pd.set_printoptions(max_columns=500)

How do I increase the number of columns displayed in Jupyter with R?

Figured it out. You can set these in options():

options(repr.matrix.max.cols=50, repr.matrix.max.rows=100)

They default to cols=20 and rows=60.

How to display all output in Jupyter Notebook within Visual Studio Code?

I think you are using the insiders build here is the right setting ,I had the same problem and it worked for me.

"notebook.output.textLineLimit": 500

edit: this will also work for the stable version

How to display full output in Jupyter, not only last result?

Thanks to Thomas, here is the solution I was looking for:

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

Importing a Dataframe from one Jupyter Notebook into another Jupyter Notebook

A direct option is to save the dataframe as a text table in the original notebook and read it into the other. Instead of plain text you can also save the dataframe itself as serialized Python for a little more efficiency/convenience.

Options from source notebook:

df.to_csv('example.tsv', sep='\t') # add `, index = False` to leave off index
# -OR-
df.to_pickle("file_name.pkl")

Options in reading notebook:

import pandas as pd
df = pd.read_csv('example.tsv', sep='\t')
#-OR-
df = pd.read_pickle("file_name.pkl")

I used tab delimited tabular text structure, but you are welcome to use comma-separated.

---

Related Topics