DataFrame:
1. DataFrame is a 2 D Data Structure.
2. DataFrame is a 2 D Structure of Python Pandas Library.
3. DataFrame is a Heterogeneous Data Structure
4. It just like a table or spreadsheet.
5. It can contains 2 or more rows and columns.
6. Types of Columns can be different.
7. Size of DataFrame is Mutable.
8. Value of DataFrame also Mutable.
9. Arithmetic Operators can be performed on rows and columns.
10. It can store different types of values.
Example:
import pandas as pd
I=['A','B','C','D','E']
D={"2019":[50,60,70,80,90],"2020":[40,35,45,55,32]}
DF=pd.DataFrame(D,I)
print(DF)
Output:
2019 2020
A 50 40
B 60 35
C 70 45
D 80 55
E 90 32
DataFrame Attributes / Properties:
DataFrame has the following attributes:
1. index
2. columns
3. axes
4. dtypes
5. size
6. shape
7. ndim
8. empty
9. count
10. T
1. index
It display the index of the DataFrame.
import pandas as pd
I=['IP','Bio','Chemistry','Physics','English']
D={"2018":[50,60,70,80,90],"2019":[40,35,45,55,32], \
"2020":[65,75,85,45,52],}
df=pd.DataFrame(D,I)
df.index.name="Subject"
print(df)
print("Index of Data Frame")
print(df.index)
Output:
2018 2019 2020
Subject
IP 50 40 65
Bio 60 35 75
Chemistry 70 45 85
Physics 80 55 45
English 90 32 52
Index of Data Frame
Index(['IP', 'Bio', 'Chemistry', 'Physics', 'English'], dtype='object', name='Subject')
2. columns
It display the name of columns of DataFrame
import pandas as pd
I=['IP','Bio','Chemistry','Physics','English']
D={"2018":[50,60,70,80,90],"2019":[40,35,45,55,32], \
"2020":[65,75,85,45,52],}
df=pd.DataFrame(D,I)
df.index.name="Subject"
print(df)
print("Columns of Data Frame")
print(df.columns)
Output:
2018 2019 2020
Subject
IP 50 40 65
Bio 60 35 75
Chemistry 70 45 85
Physics 80 55 45
English 90 32 52
Columns of Data Frame
Index(['2018', '2019', '2020'], dtype='object')
3. Axes
It display both Index name and column name of DataFrame
import pandas as pd
I=['IP','Bio','Chemistry','Physics','English']
D={"2018":[50,60,70,80,90],"2019":[40,35,45,55,32], \
"2020":[65,75,85,45,52],}
df=pd.DataFrame(D,I)
df.index.name="Subject"
print(df)
print("Axes of Data Frame")
print(df.axes)
Output:
2018 2019 2020
Subject
IP 50 40 65
Bio 60 35 75
Chemistry 70 45 85
Physics 80 55 45
English 90 32 52
Axes of Data Frame
[Index(['IP', 'Bio', 'Chemistry', 'Physics', 'English'], dtype='object', name='Subject'), Index(['2018', '2019', '2020'], dtype='object')]
4. dtype:
Display the data type of columns/Values
import pandas as pd
I=['IP','Bio','Chemistry','Physics','English']
D={"2018":[50,60,70,80.5,90],"2019":[40,35,45,55,32], \
"2020":[65,75,85,45,52],}
df=pd.DataFrame(D,I)
df.index.name="Subject"
print(df)
print("Data Type of Data Frame")
print(df.dtypes)
Output:
2018 2019 2020
Subject
IP 50.0 40 65
Bio 60.0 35 75
Chemistry 70.0 45 85
Physics 80.5 55 45
English 90.0 32 52
Data Type of Data Frame
2018 float64
2019 int64
2020 int64
dtype: object
5.size
Display the size of DataFrame i.e. total number of elements.
import pandas as pd
I=['IP','Bio','Chemistry','Physics','English']
D={"2018":[50,60,70,80.5,90],"2019":[40,35,45,55,32], \
"2020":[65,75,85,45,52],}
df=pd.DataFrame(D,I)
df.index.name="Subject"
print(df)
print("size of Data Frame")
print(df.size)
Output:
2018 2019 2020
Subject
IP 50.0 40 65
Bio 60.0 35 75
Chemistry 70.0 45 85
Physics 80.5 55 45
English 90.0 32 52
size of Data Frame
15
6.shape
Display number of rows and columns
import pandas as pd
I=['IP','Bio','Chemistry','Physics','English']
D={"2018":[50,60,70,80.5,90],"2019":[40,35,45,55,32], \
"2020":[65,75,85,45,52],}
df=pd.DataFrame(D,I)
df.index.name="Subject"
print(df)
print("Shape of Data Frame")
print(df.shape)
Output:
2018 2019 2020
Subject
IP 50.0 40 65
Bio 60.0 35 75
Chemistry 70.0 45 85
Physics 80.5 55 45
English 90.0 32 52
Shape of Data Frame
(5, 3)
Python DataFrames provide a convenient way to organize and work with structured information in a table-like format. The article explains important characteristics such as mutable rows and columns, heterogeneous data types, and useful properties including index, columns, shape, size, dtypes, and ndim. These concepts are especially useful for learners beginning to work with structured datasets and Python-based data processing.
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