AnthonyVargas85

Aggregate-in-Pandas

How to perform aggregate statistics over individual rows with the same value using groupby. How to rearrange a DataFrame into a pivot table, a great way to compare data across two dimensions.

AnthonyVargas85.github

intro-data-capstone-biodiversity

intro-data-capstone-musclehub

Introduction-to-Numpy

Arrays are a special type of list that allows us to store values in an organized manner. An array can be created by either defining it directly using np.array() or by importing a CSV using np.genfromtxt('file.csv', delimiter=','). An operation (such as addition) can be performed on every element in an array by simply performing it on the array itself. Elements can be selected from arrays using their index and array locations, both of which start at 0. Logical operations can be used to create new, more focused arrays out of larger arrays.

Introduction-to-Statistics

Using the np.sort method to locate outliers. Calculating central positions of a dataset using np.mean and np.median. Understanding the spread of our data using percentiles and the interquartile range. Finding the standard deviation of a dataset using np.std.

Learn-Matplotlib

Creating a Line Graph from Data, Changing the Appearance of the Line

Modifying-DataFrame-in-Pandas

Adding columns to a DataFrame Using lambda functions to calculate complex quantities Renaming columns

Multiple-Tables-in-SQL

JOIN will combine rows from different tables if the join condition is true. LEFT JOIN will return every row in the left table, and if the join condition is not met, NULL values are used to fill in the columns from the right table. Primary key is a column that serves a unique identifier for the rows in the table. Foreign key is a column that contains the primary key to another table. CROSS JOIN lets us combine all rows of one table with all rows of another table. UNION stacks one dataset on top of another. WITH allows us to define one or more temporary tables that can be used in the final query.

Pandas

Create a table from scratch Loading data from another file Selecting certain rows or columns of a table

AnthonyVargas85/Aggregate-in-Pandas

How to perform aggregate statistics over individual rows with the same value using groupby. How to rearrange a DataFrame into a pivot table, a great way to compare data across two dimensions.

AnthonyVargas85/AnthonyVargas85.github

AnthonyVargas85/intro-data-capstone-biodiversity

AnthonyVargas85/intro-data-capstone-musclehub

AnthonyVargas85/Introduction-to-Numpy

Arrays are a special type of list that allows us to store values in an organized manner. An array can be created by either defining it directly using np.array() or by importing a CSV using np.genfromtxt('file.csv', delimiter=','). An operation (such as addition) can be performed on every element in an array by simply performing it on the array itself. Elements can be selected from arrays using their index and array locations, both of which start at 0. Logical operations can be used to create new, more focused arrays out of larger arrays.

AnthonyVargas85/Introduction-to-Statistics

Using the np.sort method to locate outliers. Calculating central positions of a dataset using np.mean and np.median. Understanding the spread of our data using percentiles and the interquartile range. Finding the standard deviation of a dataset using np.std.

AnthonyVargas85/Learn-Matplotlib

Creating a Line Graph from Data, Changing the Appearance of the Line

AnthonyVargas85/Modifying-DataFrame-in-Pandas

Adding columns to a DataFrame Using lambda functions to calculate complex quantities Renaming columns

AnthonyVargas85/Multiple-Tables-in-SQL

JOIN will combine rows from different tables if the join condition is true. LEFT JOIN will return every row in the left table, and if the join condition is not met, NULL values are used to fill in the columns from the right table. Primary key is a column that serves a unique identifier for the rows in the table. Foreign key is a column that contains the primary key to another table. CROSS JOIN lets us combine all rows of one table with all rows of another table. UNION stacks one dataset on top of another. WITH allows us to define one or more temporary tables that can be used in the final query.

AnthonyVargas85/Pandas

Create a table from scratch Loading data from another file Selecting certain rows or columns of a table

AnthonyVargas85/python-world

AnthonyVargas85/Recreate-Graph-using-Matplotlib

AnthonyVargas85/Sample-Size-Determination

AnthonyVargas85/SQL-Aggregates

You just learned how to use aggregate functions to perform calculations on your data. What can we generalize so far? COUNT(): count the number of rows SUM(): the sum of the values in a column MAX()/MIN(): the largest/smallest value AVG(): the average of the values in a column ROUND(): round the values in the column Aggregate functions combine multiple rows together to form a single value of more meaningful information. GROUP BY is a clause used with aggregate functions to combine data from one or more columns. HAVING limit the results of a query based on an aggregate property.

AnthonyVargas85/SQL-Queries

SELECT is the clause we use every time we want to query information from a database. AS renames a column or table. DISTINCT return unique values. WHERE is a popular command that lets you filter the results of the query based on conditions that you specify. LIKE and BETWEEN are special operators. AND and OR combines multiple conditions. ORDER BY sorts the result. LIMIT specifies the maximum number of rows that the query will return. CASE creates different outputs.

AnthonyVargas85/Statistical-Concepts-for-Hypothesis-Testing

AnthonyVargas85/Statistical-Distribution-with-Numpy

What is a histogram and how to map one using Matplotlib How to identify different dataset shapes, depending on peaks or distribution of data The definition of a normal distribution and how to use NumPy to generate one using NumPy's random number functions The relationships between normal distributions and standard deviations The definition of a binomial distribution

AnthonyVargas85/Sublime-Limes-Line-Graphs

In this project, you will be acting as a data analyst for an online lime retailer called Sublime Limes. People all over the world can use this product to get the freshest, best-quality limes delivered to their door. One of the product managers at Sublime Limes would like to gain insight into the customers and their sales habits. Using Matplotlib, you'll create some different line graphs (or maybe, lime graphs) to communicate this information effectively.

AnthonyVargas85/Types-of-Hypothesis-Testing

An A/B test where half of users were shown a green submit button and the other half were shown a purple submit button. Was one group more likely to click the submit button? Men and women were both given a survey asking "Which of the following three products is your favorite?" Did the men and women have significantly different preferences? In SciPy, you can use the function chi2_contingency to perform a Chi Square test. The input to chi2_contingency is a contingency table where: The columns are each a different condition, such as men vs. women or Interface A vs. Interface B The rows represent different outcomes, like "Survey Response A" vs. "Survey Response B" or "Clicked a Link" vs. "Didn't Click" This table can have as many rows and columns as you need. In this case, the null hypothesis is that there's no significant difference between the datasets. We reject that hypothesis, and state that there is a significant difference between two of the datasets if we get a p-value less than 0.05.

AnthonyVargas85/Working-with-Multiple-

AnthonyVargas85/Working-with-Multiple-Tables-in-Pandas

Creating a DataFrame made by matching the common columns of two DataFrames is called a merge We can specify which columns should be matches by using the keyword arguments left_on and right_on We can combine DataFrames whose rows don't all match using left, right, and outer merges and the how keyword argument We can stack or concatenate DataFrames with the same columns using pd.concat