search
Search
Login
Unlock 100+ guides
menu
menu
web
search toc
close
Comments
Log in or sign up
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
What does this mean?
Why is this true?
Give me some examples!
search
keyboard_voice
close
Searching Tips
Search for a recipe:
"Creating a table in MySQL"
Search for an API documentation: "@append"
Search for code: "!dataframe"
Apply a tag filter: "#python"
Useful Shortcuts
/ to open search panel
Esc to close search panel
to navigate between search results
d to clear all current filters
Enter to expand content preview
icon_star
Doc Search
icon_star
Code Search Beta
SORRY NOTHING FOUND!
mic
Start speaking...
Voice search is only supported in Safari and Chrome.
Navigate to

Pandas DataFrame | gt method

schedule Aug 11, 2023
Last updated
local_offer
PythonPandas
Tags
mode_heat
Master the mathematics behind data science with 100+ top-tier guides
Start your free 7-days trial now!

Pandas DataFrame.gt(~) method returns a DataFrame of booleans where True indicates an entry that is strictly greater than the specified value.

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The value(s) to compare with.

2. axislink | int or string | optional

Whether to perform the comparison along the columns or the rows:

Axis

Description

"index" or 0

Compare each column.

"columns" or 1

Compare each row.

By default, axis="columns".

3. level | int or string | optional

The levels to perform comparison on. This is only relevant if your source DataFrame is a multi-index.

Return Value

A DataFrame of booleans.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4],"B":[5,6]})
df
A B
0 3 5
1 4 6

Passing in a acalar

To check for values strictly greater than 5 in the DataFrame:

df.gt(5)
A B
0 False False
1 False True

Comparing rows

By default, axis=1, which means that passing in a sequence will result in a comparison with each row:

df.gt([4,5]) # axis=1
A B
0 False False
1 False True

Here, we are comparing each row of the source DataFrame with [4,5]. This means that we are performing the following pair-wise comparisons:

(row one) [3,5] > [4,5] = [False, False]
(row two) [4,6] > [4,5] = [False, True]

We show the same df here for your reference:

df
A B
0 3 5
1 4 6

Comparing columns

By setting axis=0, we can compare each column with the specified sequence:

df.gt([4,5], axis=0)
A B
0 False True
1 False True

Here, we're performing the following pair-wise comparisons:

(column A) [3,4] > [4,5] = [False, False]
(column B) [5,6] > [4,5] = [True, True]

Case with missing values

Any comparison with missing values will result in False for that entry.

Consider the following DataFrame with a missing value:

df = pd.DataFrame({"A":[3,pd.np.nan],"B":[5,6]})
df
A B
0 3.0 5
1 NaN 6

Performing a comparison yields:

df.gt(3)
A B
0 False True
1 False True

Notice how NaN > 3 returned False.

robocat
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
thumb_up
thumb_down
Comment
Citation
Ask a question or leave a feedback...
thumb_up
0
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!