Tutorial 9- Python Pandas Tutorials In Hindi- Dataframes, Series And Dataframes Operation-Part 1

[@AdityaBonkinpelliwar]

Really thankful to you Krish. I am transitioning my career towards Data field, being with Python experience , your videos helping me a lot to understand these useful concepts. The way you explain is really awesome. This is really a great work you are doing. GBU

[@GhulamRaza-u5k]

indexing assignment to get the col 1 and col 4 we can use these two steps to get the desire outcome 1 df.iloc[:,0::3] 2 df[["Column1","Column4"]] by using these two steps we can easily get the desire outcome By the way i really enjoy learning from your videos its very informative and have learned alot

[@hritavsinghsolanki8893]

00:01 Introduction to Python Pandas 02:26 Pandas simplifies handling data in Python 06:43 Converting data to a dataframe in Python Pandas 09:13 Creating and viewing dataframes in Python Pandas 14:14 Understanding the indexing and data manipulation techniques in Pandas 16:35 The difference between Dataframes and Series in Pandas 20:28 Understanding the indexing technique using row and column index numbers 22:51 Introduction to basic operations in Pandas 27:46 Using Pandas to perform various data operations 29:48 Understanding unique values in a column.

[@vinaygairola6855]

thank you bhai

[@SpecialIdeasPreetiV]

Amazing tutorial.. everything is cleared in one go. Thankyou for such a useful video

[@HeyKaran-ir5mm]

thank you, sir... for this indirect help. and congrats for your great work. 🙏🙏👍

[@MithileshKumar-tm9yl]

Respected sir.i have read carefully your present videos.. I hope this video will be very helpful in our kvs examination...

[@akhandpratap__]

What is kvs exam?

[@comingtomorrow]

00:22:56 - cream is chessy and question is very easy , answer = df.iloc[0:5,0:4:3] .

[@saumilgupta2383]

pura column hi aa rha hai to rowindex columnindex ki kya jrurt hai sidha hi df[["Column1","Column4"]] kr do

[@awkumyoungstar2473]

In this code, iloc[:, [0, 3]] selects all rows (:) and the columns at positions 0 and 3 (which correspond to "col1" and "col4"). The resulting DataFrame, selected_columns, will contain only these two columns.

[@DanishKhan-mp1tz]

Thanku brother 😊

[@akashchaudhary9925]

try this iloc[:,::3]

[@proud_indian0161]

nice explanation, before I was really confused between these three indexing ways.

[@AnuragSharma-vi5cv]

Thanks, Krish liked the video and the concepts you taught keep doing it

[@vishalpal7968]

Rely thankful krish I'm transforming our career towards data filed it's videos help me going to Data Analytics position.

[@zonegaming3406]

bro help me plz guide me 🙏

[@RixwanKhan]

Bhai k bolna and dikhna bilkul Qaaleen bhaiya jesa hai :P Respect from Pakistan

[@codewithpranoy]

Pakistan is now Bhikaristan 😂

[@udaybhanchauhan3582]

Sb clear kr diya esi hi video search kr rha tha thanks

[@sachinkale4544]

thanksfull..very informational vedio🙏

[@sidnoga]

I really like the way you teach...

[@MrAd540]

Superb Video.Thank you so much for making the video..Kindly complete the full python video.

[@aniksamaddar1088]

And Thanks your videos are absolutely great .

[@easydatascience2508]

Hei, bro, you can watch mine too. The playlists for Python and R provide most of the fundamentals. And you can find the link to source files in video description.

[@jeekakrishna]

This is useful. Sir ek video matplotlib par bhi

[@amanagarwal721]

Thankyou so much brother😊💯

[@razanaeem7491]

What code i would write if i want to sum enrolled number of trainees in my data filtered data class status = complete and contractual trainees class status = cancelled and completed and show it college wise number

[@SaurabhSingh-sy1pe]

@17:04 type(df['column1']) type(df[['column2','column4']]) in both cases output is dataframe

[@harshGupta0103]

My Table : Column1 Column2 Column3 Column4 Column5 Row 1 0 1 2 3 4 Row 2 5 6 7 8 9 Row 3 10 11 12 13 14 Row 4 15 16 17 18 19 Row 5 20 21 22 23 24 Row 6 25 26 27 28 29 Row 7 30 31 32 33 34 Row 8 35 36 37 38 39 Row 9 40 41 42 43 44 Row 10 45 46 47 48 49 1st way : df[['Column1','Column5']] output : Column1 Column5 Row 1 0 4 Row 2 5 9 Row 3 10 14 Row 4 15 19 Row 5 20 24 Row 6 25 29 Row 7 30 34 Row 8 35 39 Row 9 40 44 Row 10 45 49 2nd way : df.iloc[:,0:5:4] output : Column1 Column5 Row 1 0 4 Row 2 5 9 Row 3 10 14 Row 4 15 19 Row 5 20 24 Row 6 25 29 Row 7 30 34 Row 8 35 39 Row 9 40 44 Row 10 45 49

[@ankeshkumar3205]

Thanks sir , This is very useful.

[@world_news261]

Great bhai ❤❤

[@dilipinamdar5523]

Thanks, Krish

[@Vaishali_kitchencorner]

df.iloc[0:1,0::4] is the answer for the indexing

[@apurbakumarmajumder9478]

thank you bhaiya for your video

[@DeepakSharma-sl5et]

Hii sir, actually when I started my pandas library and import pandas and try. to create dataframe it show pandas has no attribute of dataframe.. What can I do now??...

[@DeepakSharma-sl5et]

Please anyone help me...

[@adityatikoji1317]

@@DeepakSharma-sl5et you need to pass the data as krishan sir passes at starting of the video 0:30 go through that again other wise copy this snippet and try to paste df = (np.arange(1,20).reshape(5,4),index = ["r1","r2","r3","r4","r5"],columns=["c1","c2","c3","c4"])

[@kaushik_1809]

Thankyou sir

[@naveenrajput-z4f]

sir name df not defined error kse theek hota anyone please help me with this

[@shashi_kamal_chakraborty]

Sir, "THANKS A " + "LOT "*3 !!!

[@AbhishekRajput-bc6ux]

print(data.iloc[0:5,0::3])

[@VikasSingh-nq5yx]

In[ ] : df [[ "column1" , "column4" ]] Out[ ] : column1 Column4 Row1 0 3 Row2 4 7 Row3 8 11 Row4 12 15 Row5 16 19

[@kumaraditya5426]

Sir mere me type single wale ka pandas.core.frame.Dataframe aa raha hai

[@__________________________6910]

you said that kal hii mil jayega iske part 2 and 3. But 2 din hoo geyee, part 2 and part 3 nehi aye 😣

[@dynamicacceleration]

❤❤

[@mukultiwari5204]

Can we skip the row also??

[@adityakumartripathi8547]

there are two ways to get all those numbers from column1 and column4 In[ ] : df[['column1,'column4]] and In[ ] : df.iloc[: , : : 3]

[@aryapriyadarshanipradhan]

Hi can you please explain the slicing part

[@worldcultureisdumb2658]

Assignment answer is df.iloc[0:,0::3]

[@sanichara_]

Assignment= df.iloc[0:5, 0:4:3]

[@VikasSingh-nq5yx]

🤔🤔How?

[@sanichara_]

@@VikasSingh-nq5yx Please watch full video and try to execute :)

[@VikasSingh-nq5yx]

@@sanichara_ dekha but 0:4:3 ek sath likh skte hai kya

[@maulikpanchal1373]

a=df.iloc[:2,:] b=df.iloc[3:,:] pd.concat([a, b], axis=0)

[@shub_vyas]

By Using index values : df.iloc[:,[0,3]] By Using loc : df.loc[:,['Column1','Column4']]

[@mahboobsiddiqui1713]

Read Column : df[['col1','col4']]

[@vishalpal7968]

Sheet kha milegi

[@awkumyoungstar2473]

Assigment done by Zeeshan khattak from abdul wali khan university of Mardan pakistan dp.iloc[:, [0, 3]]

[@rechutambe8687]

df[['Column1','Column4']]

[@RanaHusnain-h1u]

30:03 / 32:47 30:03 / 32:47 😂😂

[@ashishvinod2193]

df[['Col1' , 'Col4']]

[@ajay2347]

Ans : [:,::3]

[@__________________________6910]

noice

[@VikasSingh-nq5yx]

How bro? 🤔🤔

[@KhushiKumari-wg7xw]

[0:,3:] #answer

[@priyapandey8951]

Assignment: df.iloc[0:,::3] Column1 Column4 Row1 0 3 Row2 4 7 Row3 8 11 Row4 12 15 Row5 16 19

[@programmer1305]

df.iloc [:,1::4] Correct answer

[@VikasSingh-nq5yx]

Can you teach me please double colon :: use?

[@mithunmahato309]

Start : end : step size I think .. plz correct me if wrong... I am learning programming in python

[@VikasSingh-nq5yx]

@@mithunmahato309 it's right 👌

[@VikasSingh-nq5yx]

@@mithunmahato309 mithun you on Instagram?

[@mohitujjain6562]

df.iloc[:,[0,3]] Correct ans5

[@umangsharma563]

df=pd.DataFrame(data=np.arange(0,20).reshape(5,4),index=["Row1","Row2","Row3","Row4","Row5"],columns=["columns1", "columns2", "columns3", "columns4"]) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[65], line 1 ----> 1 df=pd.DataFrame(data=np.arange(0,20).reshape(5,4),index=["Row1","Row2","Row3","Row4","Row5"],columns=["columns1", 2 "columns2", 3 "columns3", 4 "columns4"]) TypeError: 'numpy.ndarray' object is not callable

[@taimoorkhalid8054]

df5.iloc[:,[0,3]]

[@abhisheksharma10600]

df[["Column1","Column4"]]

[@MikinjMistry]

df[['colum1','colum5']]