This document provides an overview of Python basics for data analysis, including introductions to key Python packages like NumPy, Pandas, and Matplotlib. It covers fundamental Python concepts like data types, operators, conditional statements, loops and functions. It also demonstrates how to load and manipulate data with NumPy arrays and Pandas DataFrames, including indexing, slicing, grouping, merging, and handling missing values. Visualization with Matplotlib charts is also covered.

1. Python Basic for
Data Analysis
Chetan Khanzode

2. GUI
• Anaconda-navigator 1.8.7
– Use either of IDE
– Jupyter Notebook: It is an interactive computational environment, in which you
can combine code execution, rich text, mathematics, plots and rich media.
– Spyder is an open source cross-platform integrated development environment
for scientific programming in the Python language
• Download and install
– https://anaconda.org/anaconda/anaconda-
navigator

3. Why Python
• A simple language
• Free and open-source
• Ease of Portability
• Extensible and Embeddable with other
languages
• A high-level, interpreted language
• Standard libraries
• Object-oriented

4. Keywords
keywords are case sensitive

5. Help on function

6. History and path setup
Run history command to see previous commands as well as
copying executed code
Set up path on local machine so you can refer and
run the same with %run command. You need to create
MyHelloWorld.py in local path

7. Comments
One line comments are denoted by # at the start of line
Multiple line/block comments start with ''' and end with '''

8. Data structures
• Data Types
– Integer, Floating point number, Strings,
Boolean Values, Date and Timestamp
• Advanced Data Types
– Tuples , Lists, Dictionary

9. Integer , float , string

10. String
Python has very strong string processing capabilities. Subsets of strings can be
taken using the slice operator ( [ ] and [ : ] ) with indexes starting at 0 in the
beginning of the string and working their way from -1 at the end. Strings in Python
are immutable.

11. Date Time
Python has a built-in datetime module for working with dates and times

12. List
A list contains items separated by commas and enclosed within square brackets ([]).
All the items belonging to a list can be of different data type.

13. List -continue

14. Dictionary
Python's dictionaries are kind of hash tables, associative arrays with key-value pairs
Dictionaries are enclosed by curly braces ( { } ) and values can be assigned
Dictionaries accessed using square braces ( [] )

15. Tuple
Tuple is immutable collection separated by commas

16. Data conversion
To convert integer to float, use the float() function
To convert a float to an integer, the int() function.
To convert a string use str() function.

17. Operators

18. Import conventions
• Commonly used modules:
– import numpy as np
– import pandas as pd
– import matplotlib.pyplot as plt

19. Conditional if statements

20. Conditional – while and if in list

21. For loop

22. Function
• Function begin with the keyword def followed by the function name and
parentheses ( ( ) ).
• Any input parameters or arguments should be placed within these
parentheses.Arguments are specified within parentheses in function
definition separated by commas.
• It is also possible to assign default values to parameters in order to make
the program flexible and not behave in an unexpected manner.
• The code block within every function starts with a colon (:) and is indented.
• it allows you to pass any number of arguments (*argv) and you do not have
to worry about specifying the number when writing the function. This feature
becomes extremely important when dealing with lists or input data where
you do not know number of data observations beforehand.
• The statement return [expression] exits a function, optionally passing back
an expression to the caller.
• A return statement with no arguments is the same as return None.

23. Function

24. Exception
• An exception is an interruption that happens during execution of a program. When that error
occurs, Python generate an exception that can be handled, which avoid program to stop.
• We can handle exceptions using the try..except statement.
• We basically put our usual statements within the try-block
and put all our error handlers in the except-block.

25. Exception

26. File Operation

27. Classes
Python is an object oriented programming language and a Class is object constructor
The __init__() Function
All classes have a function called __init__(),
which is always executed when the class is being initiated.
The __init__() function to assign values to object properties.
The __init__() function is called automatically every time the class is being used
to create a new object

28. Numpy
• NumPy, short for Numerical Python, is the foundational package for
scientific computing in Python.
– NumPy provides basic numerical functions, especially for multi-dimensional
arrays and mathematical computation.
– SciPy builds on NumPy to provide features for scientific applications.
– a powerful N-dimensional array object
– sophisticated (broadcasting) functions, Functions for performing element-wise
computations with arrays or mathematical operations between arrays
– tools for integrating C/C++ and Fortran code
– useful linear algebra, Fourier transform, and random number capabilities
•

29. N-dimensional array
• import numpy as np
– Where np as alias
– use np.array() to create an array
– use np.arange() to create an arithmetic progression array
• ndarray: The N-dimensional array
– Use the np.array() constructor to create an
array with any number of dimensions.
– np.array(object, dtype=None)

30. N-dimensional array

31. Two dimension array slicing

32. Three Dimensional array slicing

33. Arithmetic progression

34. Array attributes
• array.ndim : The number of dimensions of this
array.
• array.shape :A tuple of the array's dimensions.
• array.dtype :The array's data type.

35. Array Method
<Array name>.astype(Transform to datatype specified)
<Arrayname>.mean() returns mean of the values in Array
<Arrayname>. Var() returns Variance of the values in array

36. Array Method
Copy and resize methods

37. Reshape Array Matrix

38. Array functions

39. Mathematical Operations
• The usual mathematical operators (+ - * /) generalize to NumPy
array
• Two vectors vector1 and vector2 of the same length, the “+”
operator gives you an element-by-element sum broadcasting

40. N-dimensional array

41. Pandas
• import pandas as pd
• import numpy as np
• path = 'C:/BigData/Python‘
• A Series is a one-dimensional array-like object containing an array of data with an associated
array of data index.
• s1 = pd.Series([1,2,4,5,6,7])

42. Pandas
• Create Custom index and access series

43. Pandas
• Adding

44. Data frames

45. Null values

46. Replace null with Mean of column
Companydf ['Fin_Department'] = Companydf['Fin_Department'].fillna(np.mean(Companydf.Fin_Department))
Companydf['Pur_Department'] = Companydf['Pur_Department'].fillna(np.mean(Companydf.Pur_Department))
Companydf['Sales_Department'] = Companydf['Sales_Department'].fillna(np.mean(Companydf.Sales_Department))

47. Data frame Missing values

48. Data frame missing values
Replace missing age value with mean of student age in class

49. Data frame missing values
• Fill null values with text

50. Data frame missing values
Replace gender missing with most frequent values

51. Data frame missing values
Replace test score based on grouping on gender and taking mean

52. Read Excel File

53. Worksheet details

54. Column Name List

55. Read excel worksheet

56. Clean the data
Drop NaN column with axis=1

57. Set index ‘Year’

58. Charts

59. Charts

60. Read the Workbook Table1

61. Rows and Columns access

62. Rows and Columns access

63. Conditional Selection

64. Add new columns in DataFrame

65. Mathematical operations

66. Value updation in Data Frame

67. Sequence creation

68. Remove column

69. Int conversion

70. Show Dataframe Values

71. Clean data and Charts

72. Dataframe from series
You can pass a number of data structures to DataFrame such as a ndarray, lists
, dict, Series, and another DataFrame

73. Transform dataframe

74. Reindex

75. Forward fill missing values

76. Backfill missing values

77. Random seed

78. lambda

79. lambda

80. lambda

81. Sum and cumsum

82. describe

83. Pandas Datareader - quandl

84. Pandas Datareader

85. Charts

86. Read CSV file

87. Clean Data

88. Charts

89. Ref
• http://www.numpy.org/
• https://digital.nhs.uk/data-and-information/publications/statistical/statistics-on-obesity-physica
• https://media.readthedocs.org/pdf/pandas-datareader/latest/pandas-datareader.pdf