Last Updated : 16 May, 2020
Improve
Improve
Like Article
Like
Save
Report
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandasis one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.corrwith()
is used to compute pairwise correlation between rows or columns of two DataFrame objects. If the shape of two dataframe object is not same then the corresponding correlation value will be a NaN
value.
Syntax: DataFrame.count(axis=0, level=None, numeric_only=False)
Parameters:
other : DataFrame
axis : 0 or ‘index’ to compute column-wise, 1 or ‘columns’ for row-wise
drop : Drop missing indices from result, default returns union of allReturns: correls : Series
Note: The correlation of a variable with itself is 1.
Example #1: Use corrwith()
function to find the correlation among two dataframe objects along the column axis
# importing pandas as pd
import
pandas as pd
# Creating the first dataframe
df1
=
pd.DataFrame({
"A"
:[
1
,
5
,
7
,
8
],
"B"
:[
5
,
8
,
4
,
3
],
"C"
:[
10
,
4
,
9
,
3
]})
# Creating the second dataframe
df2
=
pd.DataFrame({
"A"
:[
5
,
3
,
6
,
4
],
"B"
:[
11
,
2
,
4
,
3
],
"C"
:[
4
,
3
,
8
,
5
]})
# Print the first dataframe
print
(df1,
"\n"
)
# Print the second dataframe
print
(df2)
Now find the correlation among the columns of the two data frames along the row axis.
# To find the correlation among the
# columns of df1 and df2 along the column axis
df1.corrwith(df2, axis
=
0
)
Output :
The output series contains the correlation between the three columns of two dataframe objects respectively.
Example #2: Use corrwith()
function to find the correlation among two dataframe objects along the row axis
# importing pandas as pd
import
pandas as pd
# Creating the first dataframe
df1
=
pd.DataFrame({
"A"
:[
1
,
5
,
7
,
8
],
"B"
:[
5
,
8
,
4
,
3
],
"C"
:[
10
,
4
,
9
,
3
]})
# Creating the second dataframe
df2
=
pd.DataFrame({
"A"
:[
5
,
3
,
6
,
4
],
"B"
:[
11
,
2
,
4
,
3
],
"C"
:[
4
,
3
,
8
,
5
]})
# To find the correlation among the
# columns of df1 and df2 along the row axis
df1.corrwith(df2, axis
=
1
)
Output :
The output series contains the correlation between the four rows of two data frame objects respectively.
${secondsToHms(duration)}
${getTagsString(tags)}
'; } else{ let view_all_url = `${GFG_SITE_URL}videos/`; videos_content+=`
View All
`; } // videos_content+= '
'; } } return videos_content; } //function to show main video content with related videos content async function showMainVideoContent(main_video, course_link){ //Load main video $(".video-main").html(`
`); require(["ima"], function() { var player = videojs('article-video', { controls: true, // autoplay: true, // muted: true, controlBar: { pictureInPictureToggle: false }, playbackRates: [0.5, 0.75, 1, 1.25, 1.5, 2], poster: main_video['meta']['largeThumbnail'], sources: [{src: main_video['source'], type: 'application/x-mpegURL'}], tracks: [{src: main_video['subtitle'], kind:'captions', srclang: 'en', label: 'English', default: true}] },function() { player.qualityLevels(); try { player.hlsQualitySelector(); } catch (error) { console.log("HLS not working - ") } } ); const video = document.querySelector("video"); const events =[ { 'name':'play', 'callback':()=>{videoPlayCallback(main_video['slug'])} }, ]; events.forEach(event=>{ video.addEventListener(event.name,event.callback); }); }, function (err) { var player = videojs('article-video'); player.createModal('Something went wrong. Please refresh the page to load the video.'); }); /*let video_date = main_video['time']; video_date = video_date.split("/"); video_date = formatDate(video_date[2], video_date[1], video_date[0]); let share_section_content = `
${video_date}
`;*/ let hasLikeBtn = false; // console.log(share_section_content); var data = {}; if(false){ try { if((loginData && loginData.isLoggedIn == true)){ const resp = await fetch(`${API_SCRIPT_URL}logged-in-video-details/${main_video['slug']}/`,{ credentials: 'include' }) if(resp.status == 200 || resp.status == 201){ data = await resp.json(); share_section_content+= `
`; hasLikeBtn = true; } else { share_section_content+= `
`; } } else { share_section_content+= `
`; } //Load share section // $(".video-share-section").html(share_section_content); // let exitCond = 0; // const delay = (delayInms) => { // return new Promise(resolve => setTimeout(resolve, delayInms)); // } // while(!loginData){ // let delayres = await delay(1000); // exitCond+=1; // console.log(exitCond); // if(exitCond>5){ // break; // } // } // console.log(loginData); /*if(hasLikeBtn && loginData && loginData.isLoggedIn == true){ setLiked(data.liked) setSaved(data.watchlist) }*/ } catch (error) { console.log(error); } } //Load video content like title, description if(false){ $(".video-content-section").html(`
${main_video['title']}
${hideMainVideoDescription(main_video['description'], main_video['id'])}
${getTagsString(main_video['category'])} ${(course_link.length)? `
View Course
`:''} `); let related_vidoes = main_video['recommendations']; if(!!videos && videos.length>0){ //Load related videos $(".related-videos-content").html(getvideosContent()); } } //show video content element = document.getElementById('article-video-tab-content'); element.style.display = 'block'; $('.spinner-loading-overlay:eq(0)').remove(); $('.spinner-loading-overlay:eq(0)').remove(); } await showMainVideoContent(video_data, course_link); // fitRelatedVideosDescription(); } catch (error) { console.log(error); } } getVideoData(); /* $(window).resize(function(){ onWidthChangeEventsListener(); }); $('#video_nav_tab').click('on', function(){ fitRelatedVideosDescription(); });*/ });