pandasppt with informative topics coverage.pptx

pandas is the library function in python which
is used in dataframe works and data science
analytics
Prepared by,
VJ Solutions 2.0

Installation of pandas using command prompt by the following
command:
pip install pandas
Import pandas in our
program
import pandas as pd

INTRODUCTION TO PANDAS :
At the basic level ,Pandas object can be through of as enhanced
versions of NumPy arrays structured arrays in which rows and columns are
identified with labels rether than simple integer indicates.
Pandas is a Python library used for working with data sets. It has functions for
analyzing, cleaning, exploring, and manipulating data. The name "Pandas" has a reference
to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in
2008.

Benefits of Pandas
The benefits of pandas over using other language are as follows:
Data Representation: It represents the data in a form that is
suited for data analysis through its DataFrame and Series.
Clear code: The clear API of the Pandas allows you to focus on
. the core part of the code. So, it provides clear and concise code
for the user

CREATING SERIES FROM ARRAYS:
Before creating a Series, Firstly, we have to import the numpy module and then use array()
function in the program.
import pandas as pd
import numpy as np
info = np.array(['P','a','n','d','a','s'])
a = pd.Series(info)
print(a)
PROGRAM Output
0 P
1 a
2 n
3 d
4 a
5 s
dtype: object

Create a DataFrame using List:
We can easily create a DataFrame in Pandas using list.
import pandas as pd
# a list of strings
x = ['Python', 'Pandas']
# Calling DataFrame constructor on list
df = pd.DataFrame(x)
print(df)
PROGRAM
Output
0
0
Python
1 Pandas

import numpy as np
import pandas as
pd
data = pd.Series([0.25, 0.5, 0.75,
1.0])
print(data)
PROGRAM
:
OUTPUT:
0 0.25
1 0.50
2 0.75
3 1.00
dtype:
float64
As we see in the preceding output, the Series wraps both a sequence of values and a sequence of
indices, which we can access with the values and index attributes. The values are simply a familiar
NumPy array:
data.values Out[3]: array([ 0.25, 0.5 , 0.75, 1. ])

The index is an array-like object of type pd.Index, which we’ll discuss in more detail momentarily:
data.index
Like with a NumPy array, data can be accessed by the associated index via the familiar Python
square-bracket notatioN
we can also slice pandas like numpy using index value within square bracket
data[1] 0.5
data[1:3] Out[6]: 1 0.50
2 0.75
dtype: float64
As we will see, though, the Pandas Series is much more general and flexible
than the one-dimensional NumPy array that it emulates.
Out[4]: RangeIndex(start=0, stop=4,
step=1)

In[11]:
population_dict = {'California': 38332521, 'Texas': 26448193,'New York': 19651127,
'Florida': 19552860,'Illinois': 12882135}
population = pd.Series(population_dict)
print(population)
Out[11]: California 38332521
Floririda 19552860
Illinois 12882135
New York 19651127
Texas 26448193
dtype: int64
By default, a Series will be created where the index is drawn from the sorted keys.
From here, typical dictionary-style item access can be performed:
In[12]:
population['California'] Out[12]: 38332521

Unlike a dictionary, though, the Series also supports array-style operations such as
slicing:
In[13]: population['California':'Illinois']
Out[13]: California 38332521
Florida 19552860
Illinois 12882135
dtype: int64

Data Indexing and Selection:
In Chapter 2, we looked in detail at methods and tools to access, set, and modify
values in NumPy arrays.
These included indexing (e.g., arr[2, 1]), slicing (e.g., arr[:,
1:5]), masking (e.g., arr[arr > 0]), fancy indexing (e.g., arr[0, [1, 5]]), and
combinations thereof (e.g., arr[:, [1, 5]]). Here we’ll look at similar means of
accessing and modifying values in Pandas Series and DataFrame objects.
If you have used the NumPy patterns, the corresponding patterns in Pandas will
feel very familiar, though there are a few quirks to be aware of.We’ll start with the
simple case of the one-dimensional Series object, and then move on to the more
complicated two-dimensional DataFrame object.

Data Selection in Series
As we saw in the previous section, a Series object acts in many ways like a one-
dimensional NumPy array, and in many ways like a standard Python dictionary.
If we keep these two overlapping analogies in mind, it will help us to understand
the patterns of data indexing and selection in these arrays.
Series as dictionary
Like a dictionary, the Series object provides a mapping from a collection of keys
to a collection of values:
In[1]: import pandas as pd
data = pd.Series([0.25, 0.5, 0.75, 1.0],
index=['a', 'b', 'c', 'd'])
print(data)
Out[1]:
a 0.25
b 0.50
c 0.75
d 1.00
dtype:

In[2]:print( data['b']) Out[2]: 0.5
We can also use dictionary-like Python expressions and methods to examine
the keys/indices and values:
In[3]: 'a' in data
Out[3]: True
In[4]: data.keys()
Out[4]: Index(['a', 'b', 'c', 'd'], dtype='object')
In[5]: list(data.items())
Out[5]: [('a', 0.25), ('b', 0.5), ('c', 0.75), ('d', 1.0)]

Series objects can even be modified with a dictionary-like syntax. Just as you can
extend a dictionary by assigning to a new key, you can extend a Series by assigning
to a new index value:
In[6]: data['e'] = 1.25
data
Out[6]:
a 0.25
b 0.50
c 0.75
d 1.00
e 1.25
dtype: float64

Series as one-dimensional array
A Series builds on this dictionary-like interface and provides array-style item
selection via the same basic mechanisms as NumPy arrays—that is, slices,
masking, and fancy indexing.
Examples of these are as follows:
In[7]: # slicing by explicit index
data['a':'c']
Out[7]:
a 0.25
b 0.50
c 0.75
dtype: float64

In[8]: # slicing by implicit integer
index
data[0:2]
Out[8]:
a 0.25
b 0.50
dtype: float64
In[9]: # masking
data[(data > 0.3) & (data < 0.8)]
Out[9]:
b 0.50
c 0.75
dtype: float64
In[10]: # fancy indexing
data[['a', 'e']]
Out[10]:
a 0.25
e 1.25

Among these, slicing may be the source of the most confusion. Notice that
when you are slicing with an explicit index (i.e., data['a':'c']), the final index is
included in the slice, while when you’re slicing with an implicit index
(i.e.,data[0:2]), the final index is excluded from the slice.
Indexers: loc, iloc, and ix
These slicing and indexing conventions can be a source of confusion. For example,
if
your Series has an explicit integer index, an indexing operation such as data[1] will
use the explicit indices, while a slicing operation like data[1:3] will use the implicit
Python-style index.
In[11]: data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])
data
Out[11]:
1 a
3 b
5 c

In[12]: # explicit index when
indexing
data[1]
Out[12]:
'a'
In[13]: # implicit index when slicing
data[1:3]
Out[13]:
3 b
5 c
dtype: object
Because of this potential confusion in the case of integer indexes, Pandas
provides some special indexer attributes that explicitly expose certain indexing
schemes.
These are not functional methods, but attributes that expose a particular slicing
interface to the data in the Series.

First, the loc attribute allows indexing and slicing that always references the
explicit
index:
In[14]: data.loc[1]
Out[14]:
'a'
In[15]: data.loc[1:3]
Out[15]:
1 a
3 b
dtype: object
The iloc attribute allows indexing and slicing that always references the implicit
Python-style index:
In[16]: data.iloc[1]
Out[16]:
'b'
In[17]: data.iloc[1:3]
Out[17]: 3 b
5 c

Data Selection in DataFrame
Recall that a DataFrame acts in many ways like a two-dimensional or
structured array, and in other ways like a dictionary of Series structures sharing
the same index.
These analogies can be helpful to keep in mind as we explore data
selection within this structure.
DataFrame as a dictionary
The first analogy we will consider is the DataFrame as a dictionary of related
Series objects. Let’s return to our example of areas and populations of states:

In[18]:
area = pd.Series({'California': 423967, 'Texas': 695662, 'New York': 141297,
. 'Florida': 170312,'Illinois': 149995})
pop = pd.Series({'California': 38332521, 'Texas': 26448193,'New York': .
. 19651127, 'Florida': 19552860,'Illinois': 12882135})
data = pd.DataFrame({'area':area, 'pop':pop})
data
Out[18]:
area pop
California 423967 38332521
Florida 170312 19552860
Illinois 149995 12882135
New York 141297 19651127
Texas 695662 26448193

The individual Series that make up the columns of the DataFrame can be
accessed
via dictionary-style indexing of the column name:
In[19]: v=data['area']
print(v)
Out[19]:
California 423967
Florida 170312
Illinois 149995
New York 141297
Texas 695662
Name: area, dtype: int64
In[20]: data.area
Out[20]:
California 423967
Florida 170312
Illinois 149995
New York 141297
Texas 695662
Name: area, dtype: int64

In[21]: data.area is data['area']
Out[21]:
True
Though this is a useful shorthand, keep in mind that it does not work for all cases!
For example, if the column names are not strings, or if the column names conflict
with methods of the DataFrame, this attribute-style access is not possible.
For example, the DataFrame has a pop() method, so data.pop will point to this rather
than the "pop" column:
In[22]: data.pop is data['pop']
Out[22]: False

Like with the Series objects discussed earlier, this dictionary-style syntax can also be
used to modify the object, in this case to add a new column:
In[23]: data['density'] = data['pop'] / data['area']
data
Out[23]:
area pop density
California 423967 38332521 90.413926
Florida 170312 19552860 114.806121
Illinois 149995 12882135 85.883763
New York 141297 19651127 139.076746
Texas 695662 26448193 38.018740

DataFrame as two-dimensional array
As mentioned previously, we can also view the DataFrame as an enhanced
two-dimensional array. We can examine the raw underlying data array using the values
attribute:
In[24]: data.values
Out[24]: array([[ 4.23967000e+05, 3.83325210e+07, 9.04139261e+01],
[ 1.70312000e+05, 1.95528600e+07, 1.14806121e+02],
[ 1.49995000e+05, 1.28821350e+07, 8.58837628e+01],
[ 1.41297000e+05, 1.96511270e+07, 1.39076746e+02],
[ 6.95662000e+05, 2.64481930e+07, 3.80187404e+01]])
With this picture in mind, we can do many familiar array-like observations on the
DataFrame itself. For example, we can transpose the full DataFrame to swap rows and
columns:

we can transpose the full DataFrame to swap rows and columns:
In[25]: data.T
Out[25]:
California Florida Illinois New York Texas
area 4.239670e+05 1.703120e+05 1.499950e+05 1.412970e+05 6.956620e+05
pop 3.833252e+07 1.955286e+07 1.288214e+07 1.965113e+07 2.644819e+07
density 9.041393e+01 1.148061e+02 8.588376e+01 1.390767e+02 3.801874e+01
In particular, passing a single index to an array accesses a row:
In[26]: data.values[0] #we can access the single row
Out[26]:
array([ 4.23967000e+05, 3.83325210e+07, 9.04139261e+01])

Thus for array-style indexing, we need another convention. Here Pandas again uses the loc, iloc, and
ix indexers mentioned earlier. Using the iloc indexer, we can index the underlying array as if it is a
simple NumPy array (using the implicit Python-style index), but the DataFrame index and column
labels are maintained in the result:
In[28]: data.iloc[:3, :2]
Out[28]: area pop
California 423967 38332521
Florida 170312 19552860
Illinois 149995 12882135
In[29]: data.loc[:'Illinois', :'pop']
Out[29]: area pop
California 423967 38332521
Florida 170312 19552860
Illinois 149995 12882135
The ix indexer allows a hybrid of these two approaches:
In[30]: data.ix[:3, :'pop']
Out[30]: area pop
California 423967 38332521
Florida 170312 19552860
Illinois 149995 12882135

pandasppt with informative topics coverage.pptx

More Related Content

Similar to pandasppt with informative topics coverage.pptx (20)

Recently uploaded (20)

pandasppt with informative topics coverage.pptx