30- Exploratory Data Analysis (EDA) with Python (Day-14)

This video explains basic of exploratory EDA data analysis in python keeping mind the basic steps of EDA analysis, using pandas and seaborn, matplotlib libraries. How to clean data, how to remove missing values, how to filter data, how to organize your data and then how to make initial plot with data.
Learn Exploratory Data Analysis with Python in Hindi in today’s video! To keep up with the ever-evolving aspects of data and its domains, data handling and analysis has become crucial to understanding the information that comes attached to it. Exploratory data analysis uses various statistical and data visualization tools for the purpose of analyzing data to summarize its main characteristics, to make sense of the data, identify patterns and anomalies, test hypotheses and check assumptions.

only if you are interested in this 40 days long course (python_ka_chilla with baba_aammar.
More about me: I am Dr Aammar Tufail, your instructor in Python_ka_chilla. My aim is to train people in Data Science, machine learning, artificial intelligence, and deep learning by the end of the year (2022).

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# EDA analysis

[Codanics]

Please like and share the video, comment below if you have any question?

[engrismatullahkhan]

What an awesome work you are doing Sir ..
Absolutely brilliant .
Stay blessed .

[khawarabbas]

Ma sha Allah very good way and easy way of teaching

[attaullah]

Hazrat Behtareen ho gya hai. Learning and Enjoying

[faizasandhu_]

I was waiting for your video💥
And was already watching yesterday's zoom session evern after attending meeting, ky kahin kuch miss na ho gya ho😀
PS: I also have a class at 9am
But don't wanna miss these classes
Baba Aamar_ GREAT👍

[talhanisarmughal]

12:42
Brute Force technique to find the unique values of all columns
def func(att):
return titanic[att].unique()
for i in titanic.columns:
print(i, func(i))

[ahsanzafar]

while removing outliers for age column, when data filtered with age < 66, it completely removes the outliers. So, i think the technique is to change values until you get no outlier to the value at which it gives outlier, now choose a value just below it, all ouliers will be removed.

[Zeeshan]

EDA ko itna asan kar diya apny kaya bat hai Dr sahab very nice

[muhammadammarmohsin]

50:35 Correlation is a statistical term describing the degree to which two variables move in coordination with one another. If the two variables move in the same direction, then those variables are said to have a positive correlation. If they move in opposite directions, then they have a negative correlation.

[taqi_haider]

12:40 Assignment :
to find the uniques values in multiple columns
pd.concat([ks['survived'], ks['deck'], ks['Age'], ks['fare'], ks['class'], ks['survived']]).unique()

[zeeshanrafeeque]

unique values of 2 or more columns in single line of code:

pd.concat([ks['sex'], ks['pclass'], ks['who']]).unique()

[FireyLeo]

11:25
# To pull out unique values from the kashti dataset "ks", the best way to approach is by "for-loop" function
kdata = ks.columns # defining a new string to pull out the index names
for kda in kdata: # starting for -loop
print(ks[kda].unique()) # print out the values for individual unique column
print()

[faryalineurope]

In 2nd step of cleaning data, where you are trying to plot a bar graph using groupby function.
If we put () after "bar" then the code can work.

ks_clean.groupby([ 'sex', 'class' ]). mean(). plot. bar()

[ikrambashir]

12:00. Finding the code for unique values in multiple columns

output: array(['male', 'female', 'no', 'yes'], dtype=object)

[aasimnawaz]

if anyone wanna change to light theme, kindly change from (File>preferences>color theme) short key is, ctrl+k Ctrl+T

[Fayez-tkph]

Timestamp: 57:12
Assignment # 02: The log transformation has made the graph clearer and easier to understand. In other words, we can say the data is less skewed.

[atiyashaheen]

57:20 log se large number ko small numbers mein kr skte hain jis se uski graphical representation convenient ho jati hy

[natureisrelaxing]

12:09
To find the unique values of more than one columns
pd.concat([ks['sex'], ks['who'], ks['survived']]).unique()

[FireyLeo]

57:15
# for quick comparison between fare and fare_log
ks_clean.boxplot('fare')

ks_clean.boxplot('fare_log')

[resreng]

For unique values of multiple coloms
for col in ks}})