Python Pandas - Basic Functionality
By now, we learnt about the three Pandas DataStructures and how to create them. We will majorly focus on the DataFrame objects because of its importance in the real time data processing and also discuss a few other DataStructures.
Series Basic Functionality:
|
Sr.No.
|
Attribute or Method & Description
|
|
1
|
axes
Returns a list of the row axis labels
|
|
2
|
dtype
Returns the dtype of the object.
|
|
3
|
empty
Returns True if series is empty.
|
|
4
|
ndim
Returns the number of dimensions of the underlying data, by definition 1.
|
|
5
|
size
Returns the number of elements in the underlying data.
|
|
6
|
values
Returns the Series as ndarray.
|
|
7
|
head()
Returns the first n rows.
|
|
8
|
tail()
Returns the last n rows.
|
Let us now create a Series and see all the above tabulated attributes operation.
Example:
import
pandas as
pd
import
numpy as
np
#Create a series with 100 random numbers
s =
pd.
Series
(
np.
random.
randn(
4
))
print
s
Its output
is as follows −
0 0.967853
1 -0.148368
2 -1.395906
3 -1.758394
dtype: float64
axes:
Returns the list of the labels of the series.
import
pandas as
pd
import
numpy as
np
#Create a series with 100 random numbers
s =
pd.
Series
(
np.
random.
randn(
4
))
print
(
"The axes are:"
)
print
s.
axes
Its output
is as follows −
The axes are:
[RangeIndex(start=0, stop=4, step=1)]
The above result is a compact format of a list of values from 0 to 5, i.e., [0,1,2,3,4].
empty:
Returns the Boolean value saying whether the Object is empty or not. True indicates that the object is empty.
import
pandas as
pd
import
numpy as
np
#Create a series with 100 random numbers
s =
pd.
Series
(
np.
random.
randn(
4
))
print
(
"Is the Object empty?"
)
print
s.
empty
Its output
is as follows −
Is the Object empty?
False
ndim:
Returns the number of dimensions of the object. By definition, a Series is a 1D data structure, so it returns
import
pandas as
pd
import
numpy as
np
#Create a series with 4 random numbers
s =
pd.
Series
(
np.
random.
randn(
4
))
print
s
print
(
"The dimensions of the object:"
)
print
s.
ndim
Its output
is as follows −
0 0.175898
1 0.166197
2 -0.609712
3 -1.377000
dtype: float64
The dimensions of the object:
1
size:
Returns the size(length) of the series.
import
pandas as
pd
import
numpy as
np
#Create a series with 4 random numbers
s =
pd.
Series
(
np.
random.
randn(
2
))
print
s
print
(
"The size of the object:"
)
print
s.
size
Its output
is as follows −
0 3.078058
dtype: float64
The size of the object:
2
Values:
Returns the actual data in the series as an array.
import
pandas as
pd
import
numpy as
np
#Create a series with 4 random numbers
s =
pd.
Series
(
np.
random.
randn(
4
))
print
s
print
(
"The actual data series is:"
)
print
s.
values
Its output
is as follows −
0 1.787373
1 -0.605159
2 0.180477
3 -0.140922
dtype: float64
The actual data series is:
[ 1.78737302 -0.60515881 0.18047664 -0.1409218 ]
Head & Tail:
To view a small sample of a Series or the DataFrame object, use the head() and the tail() methods.
head()
returns the first n
rows(observe the index values). The
default number of elements to display is five, but you may pass a custom number.
import
pandas as
pd
import
numpy as
np
#Create a series with 4 random numbers
s =
pd.
Series
(
np.
random.
randn(
4
))
print
(
"The original series is:"
)
print
s
print
(
"The first two rows of the data series:"
)
print
s.
head(
2
)
Its output
is as follows −
The original series is:
0 0.720876
1 -0.765898
2 0.479221
3 -0.139547
dtype: float64
The first two rows of the data series:
0 0.720876
1 -0.765898
dtype: float64
tail()
returns the last n
rows(observe the index values). The default number of elements to display is five, but you may pass a custom number.
import
pandas as
pd
import
numpy as
np
#Create a series with 4 random numbers
s =
pd.
Series
(
np.
random.
randn(
4
))
print
(
"The original series is:"
)
print
s
print
(
"The last two rows of the data series:"
)
print
s.
tail(
2
)
Its output
is as follows −
The original series is:
0 -0.655091
1 -0.881407
2 -0.608592
3 -2.341413
dtype: float64
The last two rows of the data series:
2 -0.608592
3 -2.341413
dtype: float64
DataFrame Basic Functionality:
Let us now understand what DataFrame Basic Functionality is. The following tables lists down the important attributes or methods that help in DataFrame Basic Functionality.
|
Sr.No.
|
Attribute or Method & Description
|
|
1
|
T
Transposes rows and columns.
|
|
2
|
axes
Returns a list with the row axis labels and column axis labels as the only members.
|
|
3
|
dtypes
Returns the dtypes in this object.
|
|
4
|
empty
True if NDFrame is entirely empty [no items]; if any of the axes are of length 0.
|
|
5
|
ndim
Number of axes / array dimensions.
|
|
6
|
shape
Returns a tuple representing the dimensionality of the DataFrame.
|
|
7
|
size
Number of elements in the NDFrame.
|
|
8
|
values
Numpy representation of NDFrame.
|
|
9
|
head()
Returns the first n rows.
|
|
10
|
tail()
Returns last n rows.
|
Let us now create a DataFrame and see all how the above mentioned attributes operate.
Example:
import
pandas as
pd
import
numpy as
np
#Create a Dictionary of series
d =
{
'Name'
:
pd.
Series
([
'Tom'
,
'James'
,
'Ricky'
,
'Vin'
,
'Steve'
,
'Smith'
,
'Jack'
]),
'Age'
:
pd.
Series
([
25
,
26
,
25
,
23
,
30
,
29
,
23
]),
'Rating'
:
pd.
Series
([
4.23
,
3.24
,
3.98
,
2.56
,
3.20
,
4.6
,
3.8
])}
#Create a DataFrame
df =
pd.
DataFrame
(
d)
print
(
"Our data series is:"
)
print
df
Its output
is as follows −
Our data series is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
T (Transpose):
Returns the transpose of the DataFrame. The rows and columns will interchange.
import
pandas as
pd
import
numpy as
np
# Create a Dictionary of series
d =
{
'Name'
:
pd.
Series
([
'Tom'
,
'James'
,
'Ricky'
,
'Vin'
,
'Steve'
,
'Smith'
,
'Jack'
]),
'Age'
:
pd.
Series
([
25
,
26
,
25
,
23
,
30
,
29
,
23
]),
'Rating'
:
pd.
Series
([
4.23
,
3.24
,
3.98
,
2.56
,
3.20
,
4.6
,
3.8
])}
# Create a DataFrame
df =
pd.
DataFrame
(
d)
print
(
"The transpose of the data series is:"
)
print
df.
T
Its output
is as follows −
The transpose of the data series is:
0 1 2 3 4 5 6
Age 25 26 25 23 30 29 23
Name Tom James Ricky Vin Steve Smith Jack
Rating 4.23 3.24 3.98 2.56 3.2 4.6 3.8
Axes:
Returns the list of row axis labels and column axis labels.
import
pandas as
pd
import
numpy as
np
#Create a Dictionary of series
d =
{
'Name'
:
pd.
Series
([
'Tom'
,
'James'
,
'Ricky'
,
'Vin'
,
'Steve'
,
'Smith'
,
'Jack'
]),
'Age'
:
pd.
Series
([
25
,
26
,
25
,
23
,
30
,
29
,
23
]),
'Rating'
:
pd.
Series
([
4.23
,
3.24
,
3.98
,
2.56
,
3.20
,
4.6
,
3.8
])}
#Create a DataFrame
df =
pd.
DataFrame
(
d)
print
(
"Row axis labels and column axis labels are:"
)
print
df.
axes
Its output
is as follows −
Row axis labels and column axis labels are:
[RangeIndex(start=0, stop=7, step=1), Index([u'Age', u'Name', u'Rating'],
dtype='object')]
dtypes:
Returns the data type of each column.
import
pandas as
pd
import
numpy as
np
#Create a Dictionary of series
d =
{
'Name'
:
pd.
Series
([
'Tom'
,
'James'
,
'Ricky'
,
'Vin'
,
'Steve'
,
'Smith'
,
'Jack'
]),
'Age'
:
pd.
Series
([
25
,
26
,
25
,
23
,
30
,
29
,
23
]),
'Rating'
:
pd.
Series
([
4.23
,
3.24
,
3.98
,
2.56
,
3.20
,
4.6
,
3.8
])}
#Create a DataFrame
df =
pd.
DataFrame
(
d)
print
(
"The data types of each column are:"
)
print
df.
dtypes
Its output
is as follows −
The data types of each column are:
Age int64
Name object
Rating float64
dtype: object
empty:
Returns the Boolean value saying whether the Object is empty or not; True indicates that the object is empty.