Pandas Analyzing Data In Hindi

Python Pandas

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

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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