The document discusses Pandas and how it can be used to work with CSV files for machine learning. It covers reading and exploring CSV data using Pandas, including accessing elements, data types and properties. Methods for sorting and filtering CSV data like sort_values() and sort_index() are demonstrated. The document also shows how to create DataFrames from scratch or by reading CSV files, and how to add new data to existing DataFrames or CSV files.

1. Python with AI – 2
Session 2

2. Pandas - Dataframe
• Pandas is useful and important for reading CSV files, the datasets
used for training models

3. Pandas - Dataframe
• Pandas is useful and important for reading CSV files, the datasets
used for training models

4. Pandas - Dataframe
• Pandas is useful and important for reading CSV files, the datasets
used for training models

5. Pandas - Dataframe
• Concept of index in a dataframe
Index

6. Pandas - Dataframe
• Types of columns in dataframes

7. Pandas - Dataframe
• Access elements of a dataframe

8. Interpreting CSV Data - Properties
• len() - Returns the total amount of rows
• shape - Returns an object which contains the total number of rows and
columns
• head(n) - Retrieves the top n (Integer) rows
• info() - Displays all columns and their data types
• dtypes() - Retrieves the column title and its respective data type
• columns – Retrieves the column names

9. Sorting CSV Files - Methods
• Multiple different methods to sort columns and values
• sort_values() - sorting the DataFrame by one or more columns
• sort_index() - sorting the DataFrame by the row index
import pandas
nbaDataFrame = pd.read_csv("NBA_CSV_DATA.csv")
nbaDataFrame.sort_values(parameters)
nbaDataFrame.sort_index(parameters)

10. Exploring CSV File Data - Value
• Sorting columns by given player weight (decreasing to increasing)
import pandas
nbaDataFrame = pd.read_csv("NBA_CSV_DATA.csv")
sortedDataFrame = nbaDataFrame.sort_values('Weight',
ascending=True)
print sortedDataFrame[['Weight', 'Name']]

11. Exploring CSV File Data Output
Note* : Values are
sorted by row
index when values
are equal for
given sorting
factor.

12. Pandas- Methods
• Creating a dataframe from scratch

13. Sorting CSV Files - Methods
• Multiple different methods to sort columns and values
• sort_values() - sorting the DataFrame by one or more columns
• sort_index() - sorting the DataFrame by the row index
import pandas
nbaDataFrame = pd.read_csv("NBA_CSV_DATA.csv")
nbaDataFrame.sort_values(parameters)
nbaDataFrame.sort_index(parameters)

14. Exploring CSV File Data Output
Note* : Values are
sorted by row
index when values
are equal for
given sorting
factor.

15. Exploring CSV File Data - Value
• Sorting columns by given player weight (decreasing to increasing)
import pandas
nbaDataFrame = pd.read_csv("NBA_CSV_DATA.csv")
sortedDataFrame = nbaDataFrame.sort_values('Weight',
ascending=True)
print sortedDataFrame[['Weight', 'Name']]

16. Pandas- Methods
• Creating a dataframe from scratch
Note: This is useful when you want to create a
dataframe and add data to it later

17. Pandas- Methods
• Add values to a dataframe after creating it

18. Adding Elements to CSV File
• Create new data and append (add) to current CSV File
• Data is added to the end (tail) of the DataFrame
• We can use lists!
• If no value is given for a column, it is empty

19. Adding Elements to CSV File
• Creating a new DataFrame, without reading a new CSV File
dataFrame = pd.DataFrame([[Data]], columns=[Columns])
• Data and Columns are just lists!

20. Constructing our new Data Frame
• We want to add a new player (new data) to our NBA CSV file
(existing data)
Ex:
new_player_columns = ['Name', 'Team', 'Number', 'Position', 'Age',
'Height', 'Weight', 'College', 'Salary']
new_player_data = ['Ray Allen', 'Boston Celtics', 10, "C", 24, "6-
6", 190, "Boston College", 800000]

21. Adding Elements to CSV File - String Concept
firstName = "Ray"
lastName = "Allen"
fullName = firstName + " " + lastName
print(fullName)
#Output:
# Ray Allen
• We can now think about this in terms of DataFrames!

22. Creating our new Data Frame
• Now we can make our new DataFrame using the data we made
newPlayerDataFrame = pd.DataFrame([new_player_data], columns= new_player_columns)

23. Combining DataFrames Together
• concat(parameters) - Takes a list of DataFrames and combines them,
we can pass in various parameters
combinedDataFrame = pd.concat([nbaDataFrame, new_player_dataframe])
print(combinedDataFrame.tail())
*Note - We print the tail as data is added to the end.

24. Pandas- Exercise
• Create an empty dataframe with the following columns
• [`num_1`, `num_2`, `num_3`]
• Generate random numbers and add 10 rows to the dataframe

25. Statistics of columns/features

26. Statistics of columns/features – how does it
help?
• Describes the range of different features
• Understand the range of feature the machine learning algorithm trained
with
• Variation within features
• Standard deviation indicates the variation of data within the range

27. Statistics of columns/features - visualize

28. Statistics of columns/features

29. Kaggle datasets
• Mobile price prediction
• Fish Weight Prediction
• Heights and Weights
• Student performance prediction
• Predict Airline Passenger Satisfaction

30. http://aiclub.world