Good job. I am an experienced ML/DS engineer, I can like your video and recommend it to youngsters who like learning in Urdu. Keep it up bro! Answer for (18:50) Depending on the pandas version, we can use df['country'] = df['country'].astype(pd.StringDtype()) to convert to string.
Listened to it again. I understood that 'After we have recieved the data, we would need to know when and how to make use of that data and how to explore it'.
28:50 first we separate the male and female in different dataframe then we took in sns age columns from both dataframe then we can make the graph which show clearly different hist plot on the bases of gender
I tried to learn from other resources to clear my concepts but couldn't understand very well. Finally found this video and feeling quite confident about EDA now. Thank you Aammar bhai for making such masterpieces for us.
Write your name to record the attendance and like the video stream.
Muhammad Haris
Brother apka koi paid course hai kya agr ha to please suggest
Bcz usme sequence or paisa lgega to mhnt bhi hogi aache s
Ibrahim Muhammad Naeem
Muhammad Awais
EDA in python with Top 10 Steps is clear with Codanics.....Respect from Bangalore,INDIA.
Good job. I am an experienced ML/DS engineer, I can like your video and recommend it to youngsters who like learning in Urdu. Keep it up bro!
Answer for (18:50) Depending on the pandas version, we can use df['country'] = df['country'].astype(pd.StringDtype()) to convert to string.
EDA_10imp_steps with codanics are so easy..Thanks baba g
Listened to it again. I understood that 'After we have recieved the data, we would need to know when and how to make use of that data and how to explore it'.
# Step 1: Data shape (df.shape)
# Step 2: Data Structure(df.info)
# Step 3: FInd missing values (df.isnull().sum())
# Step 4: Split variables for new column / feature engineering
# Step 5: Type casting / conversion data types
# Step 6: Summary Statistics (df.describe())
# Step 7: Value count of column (df[[column_name'].value_counts())
# Step 8: Deal with duplicates | null values (means_replacement)
# Step 9: Check the normality (sns.hist(df['column'])
# Step 10: Measure the skewness and kurtosis
18:51 in astype ('str') we should use astype('string') then it will work.
informative it will be beneficial if the font zooms large.
28:50 first we separate the male and female in different dataframe then we took in sns age columns from both dataframe then we can make the graph which show clearly different hist plot on the bases of gender
such a great refresher for EDA analysis
EDA in Python with the top 10 Steps is clear with Codanics
1:07 g sunn liya hai
great work . Masha Allah ❤
I am joining for the first time. Have watched previous series already.
28.23 No need to use [[ ]] in hue. The code should be hue="sex" you will get the plot
I tried to learn from other resources to clear my concepts but couldn't understand very well. Finally found this video and feeling quite confident about EDA now. Thank you Aammar bhai for making such masterpieces for us.
1:06 , we're ready
very informative
READY for EDA
10 top steps of EDA with codanics
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1:00 ready
Tiem Stamp: 43:59 EDA in python with top ten steps, in Codenics
1:30 Sunnn lia hai
#Muhammad Hayat
In the video we will learn 10 important steps to perform EDA ...and yes we are ready
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Sir you are great
Yes I listened
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ready
@9:29 M.Mohsin ur Rehman
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# 1:08 yes dubra son leya hy sir
Azka Saleem 9:34
28:11 , sns.histplot(x='age', y='class', data=df, hue='class')
ye to pta nh kya ban gya
use this instead
sns.displot(df, x="age", hue="sex", multiple="dodge")
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Asalamo Aliakum sir ammar when will you start course deep learning ,pytorch ?
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28:14 sns.histplot(x="age",data=df,hue="sex",alpha=0.4, kde=True)
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