Pandas Analyzing Data In Hindi – Data Ko Analyzing Karna Bhi Ek Bahut Hard Kaam Hota Hai, Isliye Jab Bhi Ham Data Ko Analyzing Karne Ke Bare Me Sochte Hai, To Fir Hame Pandas Ko Istemal Karna Padta Hai, To Ham Is Post Me Data Ko Analyze Karne Wale Hai Or Dekhne Wale Hai Ki, Data Ko Kaise Analyze Karte Hai |
Agar Aapne Pandas Ki Previous Post Ko Read Nhi Kiya Hai To Aap Un Post Ko Bhi Read Kar Sakte Hai – Python Pandas Tutorial In Hindi
Pandas Analyzing Data In Hindi
Contents
Pandas Me Data 2 Types Se Analysis Kar Sakte Hai.
- Series
- DataFrames
Seriers Ka Matlab Hota Hai, 1D Array Data Or Data Frames Ka Matlab 2D or 3D Array Data, To Ham In Dono Ke Example Se Sath Pura Python Analysis Ko Cover Karne Wale Hai |
Python Series Analysis In Hindi
Sabse Pahle Ham Sereies Data Frame Ka Analysis Karne Wale Hai, To Iska Ham Niche Example Dekhte Hai |
Example 1:
import pandas as pd # Numeric data Data =[1,2,3, 4, 5] # Creating series with default index values s = pd.Series(Data) print(s)
Sabse Pahle Aapne Data Name Ek List Ko Create Kiya Or Uske Bad Hamne pandas me pd.Series(Data) Se Series Me Convert Kiya Hai.
Output:
0 1 1 2 2 3 3 4 4 5 dtype: int64
Example 2:
import pandas as pd # Numeric data Data =[1,2,3, 4, 5] # Creating series with default index values s = pd.Series(Data) # predefined index values Index =['a', 'b', 'c', 'd','e'] # Creating series with predefined index values si = pd.Series(Data, Index) si
Is Example 2 Me Hamne Data List Me Rakha Hai, Or Uske Bad Use Series Me Covert Diya Hai, Fir Hamne Or List Index Name Se Create Karke Index Number Rakhe Hai, Fir Hamne pd.Series Se Iske Data Or Index Variable Pass Kar Diye Hai, Isse Output Me Index Number Change Ho Jayenge Or Aap Example 1 Or Example 2 Me Difference Dekh Payenge |
Output:
a 1 b 2 c 3 d 4 e 5 dtype: int64
Example 3:
import pandas as pd
# Numeric data
Data =[1,2,3, 4, 5]
# Creating series with default index values
s = pd.Series(Data)
# predefined index values
Index =['a', 'b', 'c', 'd','e']
# Creating series with predefined index values
si = pd.Series(Data, Index)
print("This is Max Number :",si.max())
print("------------------------------")
print("This is Max Number :",si.min())
print("------------------------------")
print("This is for head data :",si.head(2))
print("------------------------------")
print("This is for tail Data :",si.tail(2))
print("------------------------------")
print("This is For Some Extra Information :",si.describe)
Output:
This is Max Number : 5 ------------------------------ This is Max Number : 1 ------------------------------ This is for head data : a 1 b 2 dtype: int64 ------------------------------ This is for tail Data : d 4 e 5 dtype: int64 ------------------------------ This is For Some Extra Information : <bound method NDFrame.describe of a 1 b 2 c 3 d 4 e 5 dtype: int64>
Pandas DataFrame Analysis In Hindi
Ab Ham Data Frame Yani 2D Array Or 3D Array Ko Analysis Karne Wale Hai Or Iske Bare Me Bhi Deep Me Dekhne Wale Hai |
Example 1:
# Program to create Dataframe of three series
import pandas as pd
# Define series 1
s1 = pd.Series([1, 2, 3, 4, 5])
# Define series 2
s2 = pd.Series([1.1, 2.5, 3.5, 4.7, 5.8])
# Define series 3
s3 = pd.Series(['a', 'b', 'c', 'd', 'e'])
# Define Data
Data ={'first':s1, 'second':s2, 'third':s3}
# Create DataFrame
df = pd.DataFrame(Data)
print(df)Aap Dekh Sakte Hai, Ki Hamne Isme 3 Series (s1,s2,s3) Create Ki Hai, Or Fir Hamne Data Me Dictionary Bana Ke Hamne pd.DataFrame(Data) Pass Karke DataFrame Create Kar Liya Hai |
Output:
first second third 0 1 1.1 a 1 2 2.5 b 2 3 3.5 c 3 4 4.7 d 4 5 5.8 e
Example 2:
# importing pandas library
import pandas as pd
# creating and initializing a nested list
students = [['Danish', 18, 'Bangalore', 'India',85.96],
['Riti', 30, 'Delhi', 'India',95.20]]
# Create a DataFrame object
df = pd.DataFrame(students,columns= ['Name', 'Age', 'City', 'Country','Agg_Marks'])
# Displaying the Data frame
print(df)
Output:
Name Age City Country Agg_Marks 0 Danish 18 Bangalore India 85.96 1 Riti 30 Delhi India 95.20
Example 3:
# importing pandas library
import pandas as pd
# creating and initializing a nested list
students = [['Danish', 18, 'Bangalore', 'India',85.96],
['Riti', 30, 'Delhi', 'India',95.20]]
# Create a DataFrame object
df = pd.DataFrame(students,columns= ['Name', 'Age', 'City', 'Country','Agg_Marks'])
# Displaying the Data frame
print(df.head(1))
print("---------------------------------------------------")
print(df.tail(1))
head() Function Se Ham Upper Ke Data Ko Get Karte Hai Or tail() Function Se Ham Niche Ke Data Ko Get Karte Hai |
Output:
Name Age City Country Agg_Marks 0 Danish 18 Bangalore India 85.96 --------------------------------------------------- Name Age City Country Agg_Marks 1 Riti 30 Delhi India 95.20
Example 4:
# importing pandas library
import pandas as pd
# creating and initializing a nested list
students = [['Danish', 18, 'Bangalore', 'India',85.96],
['Riti', 30, 'Delhi', 'India',95.20]]
# Create a DataFrame object
df = pd.DataFrame(students,columns= ['Name', 'Age', 'City', 'Country','Agg_Marks'])
# Displaying the Data frame
print(df.info())info() Function Se Ham Data Ke Bare Me Kuch Jyada Hi Information Get Kar Sakte Hai |
Output:
Aap Niche Dekh Sakte Hai Ki Output Me 2 Rows Aayi Hai Or 5 Column Aayi Hai, Or Value Bhi Non Null Hai, Non Null Ka Matlab Hai Ki Value Avaiable Hai, Or Uske Sath Hi Data Ka Type Bhi Show Kiya Gaya Hai, Jaise Int Ya Object Hai |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2 entries, 0 to 1 Data columns (total 5 columns): Name 2 non-null object Age 2 non-null int64 City 2 non-null object Country 2 non-null object Agg_Marks 2 non-null float64 dtypes: float64(1), int64(1), object(3) memory usage: 208.0+ bytes None
Example 5:
# importing pandas library
import pandas as pd
# creating and initializing a nested list
students = [['Danish', 18, 'Bangalore', 'India',85.96],
['Riti', 30, 'Delhi', 'India',95.20]]
# Create a DataFrame object
df = pd.DataFrame(students,columns= ['Name', 'Age', 'City', 'Country','Agg_Marks'])
# Displaying the Data frame
print(df.describe())describe() Function Se Ham Data Ke Bare Me Or Extra Information Get Kar Sakte Hai |
Output:
Age Agg_Marks count 2.000000 2.000000 mean 24.000000 90.580000 std 8.485281 6.533667 min 18.000000 85.960000 25% 21.000000 88.270000 50% 24.000000 90.580000 75% 27.000000 92.890000 max 30.000000 95.200000
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