Python Pandas DataFrame Attributes

T	Transpose , Changing Columns and indexes
at	value at input row , column
columns	Name of the Columns as List
dtypes	Data types of columns
empty	Checking if DataFrame is empty
iat	Data at position ( integer based )given as row and column
iloc	Data at (label based )
index	Details on row based
is_copy	Return the copy ( deprecated)
ix	Data based on row and column ( deprecated)
loc	Data based on (label based ) Position
ndim	Dimension ( axis ) of the DataFrame
shape	Number of rows and column as tuple
size	Number of elements in DataFrame
style	Associated HTML style
values	Numpy representation of the DataFrame

List all attributes and methods of Pandas module by using dir()

import pandas as pd
print(dir(pd))

Use this code for examples of all sample attributes shown below.

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],'MATH':[30,40,50,60,70,80],'ENGLISH':[20,30,40,50,60,70]}
my_data = pd.DataFrame(data=my_dict)
print(my_data)

We can print the output here

   NAME  ID  MATH  ENGLISH
0  Ravi   1    30       20
1  Raju   2    40       30
2  Alex   3    50       40
3   Ron   4    60       50
4  King   5    70       60
5  Jack   6    80       70

Above code will be used to check different attributes

T

T :Transpose , Changing Columns and indexes

print(my_data.T)

            0     1     2    3     4     5
NAME     Ravi  Raju  Alex  Ron  King  Jack
ID          1     2     3    4     5     6
MATH       30    40    50   60    70    80
ENGLISH    20    30    40   50    60    70

at

at : value at by row, column pair

print(my_data.at[3,'ENGLISH']) # 50

columns

columns: Name of the Columns

print(my_data.columns)

Output

Index(['NAME', 'ID', 'MATH', 'ENGLISH'], dtype='object')

dtypes

dtypes : dtypes of used DataFrame

print(my_data.dtypes)

Output

NAME       object
ID          int64
MATH        int64
ENGLISH     int64
dtype: object

empty

empty : The DataFrame empty or not ( True or False )

print(my_data.empty) # False

iat

iat : Value at position at rows and columns as integers ( inputs ).

print(my_data.iat[2,3]) # 40

iloc

iloc Values at different Positions , More on iloc

index

index : Details on row labels

print(my_data.index) # RangeIndex(start=0, stop=6, step=1)

is_copy

is_copy : deprecated

ix

ix : deprecated , position based on row and column

print(my_data.ix[2,'MATH']) # 50

loc

loc : Values , More on loc

ndim

ndim : array dimensions or axes

print(my_data.ndim) #2

shape

shape : Tuple giving dimension of DataFrame as ( rows, columns )

print(my_data.shape) # (6,4)

size

size : Number of elements in the DataFrame

print(my_data.size) #24

Watch the difference between my_data.shape and my_data.size here.
As shape returns the number of rows and columns, we can multiply them to get number of elements which we can also get by using size.

Details on shpe size and ndim

style

style : Associated html style

print(my_data.style)

values

values : All values of the DataFrame without axes labels. Numpy representation of the DataFrame.

print(my_data.values)

Output is here

[['Ravi' 1 30 20]
 ['Raju' 2 40 30]
 ['Alex' 3 50 40]
 ['Ron' 4 60 50]
 ['King' 5 70 60]
 ['Jack' 6 80 70]]

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