On this story, I’ll clarify the right way to view all objects. columns and rows of a Pandas DataFrame. I can even clarify the right way to show all values ​​in a listing in a DataFrame and the way to decide on the precision of numbers in a DataFrame. And you are able to do all of it with the identical software.

I’ll use the dataset of the highest 250 IMDB motion pictures downloaded from: Data World. The database has 250 rows and 37 columns.

Downside: Pandas Cuts Info

Typically you may learn with a DataFrame many rows or columnshowever once you view it in Jupyter, the rows and columns are hidden (highlighted in pink containers):

motion pictures = pd.read_csv("information/IMDB_Top250movies2_OMDB_Detailed.csv")
motion pictures

They’re normally hidden in order to not present an excessive amount of data. However typically it’s possible you’ll wish to see all columns and rows. So how will we print all of them?

To do that we have to mess around with the choices parameters. pandas Let’s examine.

Extra on pandas: Sorting Data Frames in Pandas: A Hands-On Guide

Present All Columns and Rows in Pandas

Now I’ll go over the completely different instructions you need to use to alter the variety of columns and rows you may see in Pandas, for instance:

• show.max_columns
• max_colwidth
• max_rows and min_rows
• max_seq_items 

choose Alternative

there are pandas options configuration Menu that means that you can change the show settings of your DataFrame (and extra).

All it’s a must to do is select your choice with a string title and get/set/reset its values. These capabilities settle for an everyday expression sample, so in case you move a substring, it can work so long as a number of choices do not match.

view columns

This show.max_columns choice controls the variety of columns to print. takes one int or Nonethe second is used to print all columns):

pd.set_option('show.max_columns', None)
motion pictures.head()

You may as well use string max_columns instead show.max_columns. Word that it accepts an everyday expression:

pd.set_option('max_columns', None)

To move a quantity as a substitute of “None”, enter:

pd.set_option('max_columns', 2)
motion pictures.head()

To revert to the default worth, it’s essential reset the choice:

pd.reset_option(“max_columns”)
motion pictures.head()

Extra on pandas: Clipboard to DataFrame with Pandas: A Quick Guide

arrange column width

You possibly can change the column width with the choice. max_colwidth. For instance, the “plot” column has a number of characters, and the picture was initially lower:

You possibly can enhance the width. int. Or put it on max move None:

pd.set_option(“max_colwidth”, None)
motion pictures[[“Title”, “Plot”]].head()

Altering the Variety of Rows

To vary the variety of rows it’s essential change max_rows selection.

pd.set_option("max_columns", 2) #Exhibiting solely two columns
pd.set_option("max_rows", None)
motion pictures

There are two settings concerning rows: max_rows and min_rows. When the variety of rows is multiple max_rowsThe DataFrame is truncated and reveals: min_rows rows.

For instance, let’s reprint DataFrame motion pictures with default values. max_rows and min_rows:

print("Default max_rows: {} and min_rows: {}".format(
pd.get_option("max_rows"), pd.get_option("min_rows")))
motion pictures

Because the variety of rows within the dataframe is 250 max_rows A price of 60 signifies the primary and final 5 strains; min_rows The worth we set as 10.

if we alter min_rows by two, it shows solely the primary and final strains:

pd.set_option(“min_rows”, 2)
motion pictures

If we use head command with a worth under max_rows worth (60), all rows are proven. For instance, utilizing head With a worth of 20:

motion pictures.head(20)

Set the Order of Objects

The order of things (lists) can even be truncated in the event that they comprise too many characters:

#Create "my_list" column and put a listing of 100 values in every row
motion pictures[‘my_list’] = [[1]*100] * 250 
motion pictures.head()

Possibility to alter this conduct max_seq_items. However we even have to alter max_colwidth. if we alter max_colwidth, The lists can be shortened:

pd.set_option(“max_colwidth”, None)
motion pictures.head()

So, you’ll need to alter max_seq_item.

pd.set_option(“max_seq_item”, None)
motion pictures.head()

Extra on pandas: Pandas Pivot Table Guide

Precision of numbers

One other helpful choice is to set the floating level precision or the variety of digits after the decimal utilizing the precision choice.

#including extra decimal locations to imdbRating column
motion pictures[‘imdbRating’] = motion pictures[‘imdbRating’] + 0.11111
motion pictures[[‘imdbRating’]].head()

pd.set_option(‘precision’, 2)
motion pictures[[‘imdbRating’]].head()