TODO 3 While we are focused on the 'month' and 'day' features let's take a closer at them see if we can gain any further insights about our data. For each TODO use either iloc, loc or square brackets [ ] to slice the forestfire_df DataFrame. Slice the 'month' column from our forestfire_df feature and call the value_counts() method on said slice to get the number of times each month appears in our data. Store the output into the variable month_counts. Slice the 'day' column from our forestfire_df feature and call the value_counts() method on said slice to get the number of times each day appears in our data. Store the output into the variable day_counts. # TODO 3.1 month_counts = display(month_counts) todo_check([ (np.all(month_counts.values == np.array([184, 172, 54, 32, 20, 17, 15, 9, 9, 2, 2, 1])), 'Month values did not match!') ]) # TODO 3.2 day_counts = display(day_counts) todo_check([ (np.all(day_counts.values == np.array([95, 85, 84, 74, 64, 61, 54])), 'Month values did not match!') ])
SQL
SQL stands for Structured Query Language, is a form of communication that uses queries structured in a specific format to store, manage & retrieve data from a relational database.
Queries
A query is a type of computer programming language that is used to retrieve data from a database. Databases are useful in a variety of ways. They enable the retrieval of records or parts of records, as well as the performance of various calculations prior to displaying the results. A search query is one type of query that many people perform several times per day. A search query is executed every time you use a search engine to find something. When you press the Enter key, the keywords are sent to the search engine, where they are processed by an algorithm that retrieves related results from the search index. Your query's results are displayed on a search engine results page, or SER.
TODO 3
While we are focused on the 'month' and 'day' features let's take a closer at them see if we can gain any further insights about our data. For each TODO use either iloc, loc or square brackets [ ] to slice the forestfire_df DataFrame.
-
Slice the 'month' column from our forestfire_df feature and call the value_counts() method on said slice to get the number of times each month appears in our data. Store the output into the variable month_counts.
-
Slice the 'day' column from our forestfire_df feature and call the value_counts() method on said slice to get the number of times each day appears in our data. Store the output into the variable day_counts.
# TODO 3.1
month_counts =
display(month_counts)
todo_check([
(np.all(month_counts.values == np.array([184, 172, 54, 32, 20, 17, 15, 9, 9, 2, 2, 1])), 'Month values did not match!')
])
# TODO 3.2
day_counts =
display(day_counts)
todo_check([
(np.all(day_counts.values == np.array([95, 85, 84, 74, 64, 61, 54])), 'Month values did not match!')
])
Introduction
iLOC function:
The iloc function is a way of accessing specific indices or ranges of indices in a Pandas DataFrame. iloc stands for integer location, which means it accesses the data based on its index position in the DataFrame.
For example, df.iloc[3] would return the 4th row of the DataFrame df, and df.iloc[2:5] would return the 3rd to the 5th rows of the DataFrame df. iloc is a useful method to access specific data in a DataFrame based on its position.
LOC function:
The loc function is a method of Pandas DataFrames and is used for indexing and slicing based on index label instead of integer location. For example, if the index of a DataFrame is set to a string value such as "Name", then you can use the loc method to select rows based on the values in the "Name" column. The syntax is similar to using square brackets, df.loc[row_index, column_index].
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