1 
Introduction to Data Science 

2 
Introduction to Pandas in Python 

3 
How to Install Python Pandas on Windows and Linux?


4 
How To Use Jupyter Notebook: An Ultimate Guide 

5 
Python → Pandas DataFrame 

6 
Creating a Pandas DataFrame 

7 
Python → Pandas Series 

8 
Creating a Pandas Series 

9 
View the top rows of the frame 

10 
View the bottom rows of the frame 

11 
View basic statistical details 

12 
Convert the pandas DataFrame to numpy Array 

13 
Convert the pandas Series to numpy Array 

14 
Convert series or dataframe object to Numpyarray using .as_matrix() 

15 
Dealing with Rows and Columns in Pandas DataFrame 

16 
How to select multiple columns in a pandas dataframe 

17 
Python → Pandas Extracting rows using .loc[] 

18 
Python → Extracting rows using Pandas .iloc[] 

19 
Indexing and Selecting Data with Pandas 

20 
Boolean Indexing in Pandas 

21 
Label and Integer based slicing technique using DataFrame.ix[] 

22 
Adding new column to existing DataFrame in Pandas 

23 
Python → Delete rows/columns from DataFrame 

24 
Truncate a DataFrame before and after some index value 

25 
Truncate a Series before and after some index value 

26 
Iterating over rows and columns in Pandas DataFrame 

27 
Working with Missing Data in Pandas 

28 
Sorts a data frame in Pandas → Set1 

29 
Sorts a data frame in Pandas → Set2 

30 
Pandas GroupBy 

31 
Grouping Rows in pandas 

32 
Combining multiple columns in Pandas groupby with dictionary 

33 
Python → Pandas Merging, Joining, and Concatenating 

34 
Concatenate Strings 

35 
Append rows to Dataframe 

36 
Concatenate two or more series 

37 
Append a single or a collection of indices 

38 
Combine two series into one 

39 
Add a row at top in pandas DataFrame 

40 
Join all elements in list present in a series 

41 
Join two text columns into a single column in Pandas 

42 
Python → Working with date and time using Pandas 

43 
Timestamp using Pandas 

44 
Current Time using Pandas 

45 
Convert timestamp to ISO Format 

46 
Get datetime object using Pandas 

47 
Replace the member values of the given Timestamp 

48 
Convert string Date time into Python Date time object using Pandas 

49 
Get a fixed frequency DatetimeIndex using Pandas 

50 
Python → Pandas Working With Text Data 

51 
Convert String into lower, upper or camel case 

52 
Replace Text Value 

53 
Replace Text Value using series.replace() 

54 
Removing Whitespaces 

55 
Move dates forward a given number of valid dates using Pandas 

56 
Read csv using pandas 

57 
Saving a Pandas Dataframe as a CSV 

58 
Loading Excel spreadsheet as pandas DataFrame 

59 
Creating a dataframe using Excel files 

60 
Working with Pandas and XlsxWriter → Set – 1 

61 
Working with Pandas and XlsxWriter → Set – 2 

62 
Working with Pandas and XlsxWriter → Set – 3 

63 
Apply a function on the possible series 

64 
Apply function to every row in a Pandas DataFrame 

65 
Apply a function on each element of the series 

66 
Aggregation data across one or more column 

67 
Mean of the values for the requested axis 

68 
Mean of the underlying data in the Series 

69 
Mean absolute deviation of the values for the requested axis 

70 
Mean absolute deviation of the values for the Series 

71 
Unbiased standard error of the mean 

72 
Find the Series containing counts of unique values 

73 
Find the Series containing counts of unique values using Index.value_counts() 

74 
Pandas Builtin Data Visualization 

75 
Data analysis and Visualization with Python → Set 1 

76 
Data analysis and Visualization with Python → Set 2 

77 
Box plot visualization with Pandas and Seaborn 

78 
How to Do a vLookup in Python using pandas 

79 
Convert CSV to HTML Table in Python 

80 
KDE Plot Visualization with Pandas and Seaborn 

81 
Analyzing selling price of used cars using Python 

82 
Add CSS to the Jupyter Notebook using Pandas 

Stimated Required time for this course is: 60 hours 