Showing posts with label Data Science. Show all posts
Showing posts with label Data Science. Show all posts
Friday, June 12, 2020
Wednesday, October 16, 2019
Data Science - Pandas- Series and Dataframe
Thsi Post have the complete syntax and code wise details of Pandas two Major concept Series and Data Frame.
Now Lets Start-
Now Lets Start-
# Pandas Series
#Series is a one-dimensional labeled array capable of
#holding data of any type (integer, string, float, python objects, etc.).
# The axis labels are collectively called index
In [1]:
1 import pandas as pd
2 import numpy as np
In [2]:
# A pandas Series can be created using the following constructor −
# pandas.Series( data, index, dtype, copy)
3 df = pd.Series()
4 df
Out[2]:
Series([], dtype: float64)
In [3]:
# A series can be created using various inputs like −
# Array
# Dict
# Scalar value or constant
In [4]:
# Create a Series from ndarray
data = np.array(['D','I','Z','S','W','E','B'])
s = pd.Series(data)
s
Out[4]:
0 D 1 I 2 Z 3 S 4 W 5 E 6 B dtype: object
In [6]:
# Create a Series from ndarray
data = np.array(['D','I','Z','S','W','E','B'])
s = pd.Series(data, index =['001','002','003','004','005','006','007'])
s
Out[6]:
001 D 002 I 003 Z 004 S 005 W 006 E 007 B dtype: object
In [7]:
# Create a Series from dict
data = {'D':1,'I':2,'Z':3,'S':4,'W':5.0,'E':6,'B':7.0}
s = pd.Series(data)
s
Out[7]:
D 1.0 I 2.0 Z 3.0 S 4.0 W 5.0 E 6.0 B 7.0 dtype: float64
In [8]:
# Create a Series from dict
data = {'D':1,'I':2,'Z':3,'S':4,'W':5.0,'E':6,'B':7.0}
s = pd.Series(data, index =['001','002','003','004','005','006','007'])
s
# output will be NaN becuase in case of dictionary
# if the values not match with index it gices NaN
Out[8]:
001 NaN 002 NaN 003 NaN 004 NaN 005 NaN 006 NaN 007 NaN dtype: float64
In [9]:
# Create a Series from dict
data = {'D':1,'I':2,'Z':3,'S':4,'W':5.0,'E':6,'B':7.0}
s = pd.Series(data, index =['D','002','Z','004','W','006','B'])
s
Out[9]:
D 1.0 002 NaN Z 3.0 004 NaN W 5.0 006 NaN B 7.0 dtype: float64
In [12]:
# Create a Series from Scalar
s = pd.Series(3, index = ['1','2','3','4','5','6'])
s
Out[12]:
1 3 2 3 3 3 4 3 5 3 6 3 dtype: int64
In [13]:
# Create a Series from Scalar without index
s = pd.Series(3)
s
Out[13]:
0 3 dtype: int64
In [14]:
# Accessing Data from Series with Position
s = pd.Series([1,2,3,4,5,6,7],index = ['D','I','Z','S','W','E','B'])
#retrieve the first three element
print s[:3]
File "<ipython-input-14-fbdfbded2fb7>", line 2 s = pd.Series([1,2,3,4,5,,6,7],index = ['D','I','Z','S','W','E','B']) ^ SyntaxError: invalid syntax
In [16]:
# Accessing Data from Series with Position
s = pd.Series([1,2,3,4,5,6,7],index = ['D','I','Z','S','W','E','B'])
# Retrieve the first three element
print(s[:3])
D 1 I 2 Z 3 dtype: int64
In [17]:
# Retrieve the last three elements.
print(s[-3:])
W 5 E 6 B 7 dtype: int64
In [18]:
# Retrieve Data Using Label (Index)
print(s['D'])
In [19]:
# Retrieve multiple elements using a list of index label values.
print(s[['D','Z','S','W']])
D 1 Z 3 S 4 W 5 dtype: int64
In [20]:
# If a label is not contained, an exception is raised.
print(s[['D','Z','S','W','Chauhan']])
D 1.0 Z 3.0 S 4.0 W 5.0 Chauhan NaN dtype: float64
c:\users\hp\appdata\local\programs\python\python37-32\lib\site-packages\pandas\core\series.py:1152: FutureWarning: Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative. See the documentation here: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike return self.loc[key]
In [ ]:
# DataFrame
# Defination-A Data frame is a two-dimensional data structure,
# i.e., data is aligned in a tabular fashion in rows and columns
# Features of DataFrame
# Potentially columns are of different types
# Size – Mutable
# Labeled axes (rows and columns)
# Can Perform Arithmetic operations on rows and columns
# A pandas DataFrame can be created using the following constructor −
# pandas.DataFrame( data, index, columns, dtype, copy)
In [21]:
# How to create the Pandas DataFrame?
In [22]:
# Their are following methods to create the Pandas DataFrame.
# Lists
# dict
# Series
# Numpy ndarrays
# Another DataFrame
# Now lets us start the learning this with
In [23]:
import pandas as pd
import numpy as np
# define the dataframe object
df = pd.DataFrame()
# lets print this
df
Out[23]:
In [24]:
print(df)
Empty DataFrame Columns: [] Index: []
In [27]:
1 import pandas as pd
2 import numpy as np
3
4 # define the dataframe object
5 data = [1,2,3,4,5,6,7]
6 df = pd.DataFrame(data)
7
8 # lets print this
9 print(df)
0 0 1 1 2 2 3 3 4 4 5 5 6 6 7
In [28]:
1 df
Out[28]:
0
0 1
1 2
2 3
3 4
4 5
5 6
6 7
In [29]:
1 import pandas as pd
2 import numpy as np
3
4 # define the dataframe object
5 data = [['Sawan',26],['Sandeep',25],['Shiv',24]]
6 df = pd.DataFrame(data,columns=['Name','Age'])
7
8 # lets print this
9 print(df)
Name Age 0 Sawan 26 1 Sandeep 25 2 Shiv 24
In [31]:
1 import pandas as pd
2 import numpy as np
3
4 # define the dataframe object
5 data = [['Sawan',26],['Sandeep',25],['Shiv',24]]
6 df = pd.DataFrame(data,columns=['Name','Age'], dtype=int)
7
8 # lets print this
9 print(df)
Name Age 0 Sawan 26 1 Sandeep 25 2 Shiv 24
In [36]:
1 # Create a DataFrame from Dict of ndarrays / Lists
2 import pandas as pd
3 import numpy as np
4
5 # define the dataframe object
6 data = {'Name':['Dizsweb','Sawan','Chauhan'] ,'Age' :[ 123, 26, 35]}
7 df = pd.DataFrame(data)
8
9 # lets print this
10 print(df)
Name Age 0 Dizsweb 123 1 Sawan 26 2 Chauhan 35
In [37]:
1 # Create a DataFrame from Dict of ndarrays / Lists
2 import pandas as pd
3 import numpy as np
4
5 # define the dataframe object
6 data = {'Name':['Dizsweb','Sawan','Chauhan'] ,'Age' :[ 123, 26, 35]}
7 df = pd.DataFrame(data, index =['R1','R2','R3'])
8
9 # lets print this
10 print(df)
Name Age R1 Dizsweb 123 R2 Sawan 26 R3 Chauhan 35
In [39]:
1 # Create a DataFrame from List of Dicts
2 import pandas as pd
3 import numpy as np
4 data = [{'a':3,'b':4,'c':8},{'a':5,'d':6,'f':9},{'b':3,'c':8,'d':9}]
5 df = pd.DataFrame(data)
6 print(df)
a b c d f 0 3.0 4.0 8.0 NaN NaN 1 5.0 NaN NaN 6.0 9.0 2 NaN 3.0 8.0 9.0 NaN
In [44]:
1 # Creation of two Dataframe from the same data
2 import pandas as pd
3 data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]
4
5 #With two column indices, values same as dictionary keys
6 df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b'])
7
8 #With two column indices with one index with other name
9 df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])
10 print(df1)
11
12 print(df2)
a b first 1 2 second 5 10 a b1 first 1 NaN second 5 NaN
In [47]:
1 # Create a DataFrame from Dict of Series
2 d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
3 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
4
5 df = pd.DataFrame(d)
6 print(df)
7
one two a 1.0 1 b 2.0 2 c 3.0 3 d NaN 4
In [48]:
1 # perticular column Selection
2 print(df['one'])
a 1.0 b 2.0 c 3.0 d NaN Name: one, dtype: float64
In [50]:
1 # Column Addition
2 import pandas as pd
3
4 d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
5 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
6
7 df = pd.DataFrame(d)
8
9 # Adding a new column to an existing DataFrame object with column label by passing new series
10
11 print ("Adding a new column by passing as Series:")
12 df['three']=pd.Series([10,20,30],index=['a','b','c'])
13 print(df)
14
15 print("Adding a new column using the existing columns in DataFrame:")
16 df['four']=df['one']+df['three']
17
18 print(df)
Adding a new column by passing as Series: one two three a 1.0 1 10.0 b 2.0 2 20.0 c 3.0 3 30.0 d NaN 4 NaN Adding a new column using the existing columns in DataFrame: one two three four a 1.0 1 10.0 11.0 b 2.0 2 20.0 22.0 c 3.0 3 30.0 33.0 d NaN 4 NaN NaN
In [51]:
1 # Column Deletion
2 # Using the previous DataFrame, we will delete a column
3 # using del function
4 import pandas as pd
5
6 d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
7 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']),
8 'three' : pd.Series([10,20,30], index=['a','b','c'])}
9
10 df = pd.DataFrame(d)
11 print("Our dataframe is:")
12 print(df)
13
14 # using del function
15 print("Deleting the first column using DEL function:")
16 del(df['one'])
17 print(df)
18
19 # using pop function
20 print("Deleting another column using POP function:")
21 df.pop('two')
22 print(df)
Our dataframe is: one two three a 1.0 1 10.0 b 2.0 2 20.0 c 3.0 3 30.0 d NaN 4 NaN Deleting the first column using DEL function: two three a 1 10.0 b 2 20.0 c 3 30.0 d 4 NaN Deleting another column using POP function: three a 10.0 b 20.0 c 30.0 d NaN
In [55]:
1 # Row Selection, Addition, and Deletion with loc()
2 d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
3 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
4
5 df = pd.DataFrame(d, dtype=int)
6 print(df.loc['b'])
one 2.0 two 2.0 Name: b, dtype: float64
In [57]:
1 # Row Selection, Addition, and Deletion with iloc()
2 d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
3 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
4
5 df = pd.DataFrame(d, dtype=int)
6 print(df.iloc[2])
one 3.0 two 3.0 Name: c, dtype: float64
In [58]:
1 # Slice Rows
2 import pandas as pd
3
4 d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
5 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
6
7 df = pd.DataFrame(d)
8 print(df[2:4])
one two c 3.0 3 d NaN 4
In [63]:
1 # Addition of Rows using append funtion
2
3 import pandas as pd
4
5 df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])
6 df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])
7
8 df = df.append(df2)
9 print(df)
a b 0 1 2 1 3 4 0 5 6 1 7 8
In [64]:
1 # Deletion of Rows
2
3 import pandas as pd
4
5 df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])
6 df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])
7
8 df = df.append(df2)
9
10 # Drop rows with label 0
11 df = df.drop(0)
12
13 print(df)
a b 1 3 4 1 7 8
In [65]:
1 # Python Pandas - Panel
2 # A panel is a 3D container of data.
3 # The term Panel data is derived from econometrics and
4 # is partially responsible for the name pandas − pan(el)-da(ta)-s.
5
6 # The names for the 3 axes are intended to give
7 # some semantic meaning to describing operations
8 # involving panel data. They are −
9
10 # items − axis 0, each item corresponds to a DataFrame contained inside.
11 # major_axis − axis 1, it is the index (rows) of each
of the DataFrames.
12
# minor_axis − axis 2, it is the columns of each of the DataFrames.
13
14
# pandas.Panel()
15
# A Panel can be created using the following constructor −
16
# pandas.Panel(data, items, major_axis, minor_axis, dtype, copy)
17
# This class is removed from pandas now
Wednesday, October 9, 2019
Indexes in Python
Indexing is a standard feature of most programming languages. Java, Python, C#,
C++, and C all have syntax like E[i] whereby an item with index i can be
reference in E (which might be a character string, an array of numbers etc).
Python goes further beyond, it is possible to refer to chunks of a sequence instead of indexing items one at a time. This feature in python is called slicing a sequence. Other techniques, like split and join, make it easy to take apart strings and assemble lists of strings into a new strings . In the same way that hand tools allow people to cut, shape, reshape, and put together wood into furniture, slicing, split, and join operations form the “carpentry” of Python’s text processing tasks. Even more powerful methods, notabe regular expressions, could be likened to the power tools of text and string processing. This post only covers the basic techniques, leaving regular expressions and other more advance libraries of software as later topics to explore. Working with slices particularly in Python’s syntax for slices, takes some getting used to. With regular practice, it becomes easy to write expressions that take apart sequences in creative ways and use operators to assemble slices into a new sequences. For strings slicing is only the beginning. String methods provide ways to search, replace, trim and form new strings from sequences of strings.
Following three methods are used to get the data in python via indexing
1 loc() Only for lables
2 iloc() only for integer
3 ix() for both label and and integer.
Python goes further beyond, it is possible to refer to chunks of a sequence instead of indexing items one at a time. This feature in python is called slicing a sequence. Other techniques, like split and join, make it easy to take apart strings and assemble lists of strings into a new strings . In the same way that hand tools allow people to cut, shape, reshape, and put together wood into furniture, slicing, split, and join operations form the “carpentry” of Python’s text processing tasks. Even more powerful methods, notabe regular expressions, could be likened to the power tools of text and string processing. This post only covers the basic techniques, leaving regular expressions and other more advance libraries of software as later topics to explore. Working with slices particularly in Python’s syntax for slices, takes some getting used to. With regular practice, it becomes easy to write expressions that take apart sequences in creative ways and use operators to assemble slices into a new sequences. For strings slicing is only the beginning. String methods provide ways to search, replace, trim and form new strings from sequences of strings.
Following three methods are used to get the data in python via indexing
1 loc() Only for lables
2 iloc() only for integer
3 ix() for both label and and integer.
Sunday, October 6, 2019
Linear Programming Problem (LPP) Learnig Example with more than 2 varibles For Data Science Biggner in Python and Jupyter
In this Post we will learn lot of Linear Programming Problem Example. I will try to provide the coding part of problem not the lengthy documentation.
Now lets Start with our Examples which is generally asked in various unveristy exams also.
Example 1:-
A Manufacturing compamy is engaged in producing three types of product L, M and N. The production department produces, each day, components sufficent to make 50 units of L, 25 Units of M and 30 units of N.
The management is confronted with the problem of optimizing the daily production of the prodccts in the assembly department, where only 100 man hours are availble daily for assembly the products.
Following addtional information is available-
| Type of Product | Profit Contribution per Unit of Products (RS.) | Assemble Time per product (hrs) |
| L | 12 | 0.8 |
| M | 20 | 1.7 |
| N | 45 | 2.5 |
The Company has a daily order commitment for 20 units of product L and a total of 15 units of M and N.
Now we have to Formulate the Linear programming model as maximize the total profit.
Now LP Model formulation of the is problem is -
| Constraints | Product Type | Total | ||
| L | M | N | ||
| Production Capacity | 50 | 25 | 30 | |
| Man Hours per Unit | 0.8 | 1.7 | 2.5 | 100 |
| Order commitment | 20 | 15( both of M and N) | ||
| Profit Contribution | 12 | 20 | 45 | |
Maximize Or Objective Function above Problem is -
Z=12 X1 + 20 X2 + 45 X3 or
Z=12x+20y+24z
Comstraints-
(A) 0.8X1 + 1.7X2 +2.5 X3 <=100
(B) X1 <= 50
(C) X2 <= 25
(D) X3 <= 30
ORDER COMMITMENT
(A) X1 >= 20
(B) X2 + X3 >=15
(C) X1, X2, X3 >=0
This is the Complete description of our Problem, Now lets start the coding in python to get the final output of problem.
Solution -
As per our profit contribution we should have minimum production of product L, then M and maximum of N.
Now we have to find the coordinates.
1- X1=(0,20) and (0,50)
2- X2= (0,15)
3- X3= (0,15)
Solution -
As per our profit contribution we should have minimum production of product L, then M and maximum of N.
Now we have to find the coordinates.
1- X1=(0,20) and (0,50)
2- X2= (0,15)
3- X3= (0,15)
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