Analyzing Your Data Using Basic Statistics: Descriptive Statistics

Analyzing Statistics Your Data Using Basic Statistics: Descriptive It's important to first investigate your data when you start data analysis before you invest time in creating intricate models. Doing some descriptive statistical calculations for your data is a simple method to accomplish this. A brief overview of the sample and data measurements is obtained by descriptive statistical analysis, which also aids in describing the fundamental characteristics of a dataset. In this post, we'll talk about how basic statistics combined with descriptive statistics can help you comprehend your data. Using Pandas' description function Using Pandas' "describe" function is one way we can accomplish this. The describe function automatically computes the fundamental statistics for all numerical variables when used on your data frame. The mean, total number of data points, standard deviation, quartiles, and extreme values are all displayed. These statistics automatically omit any "NaN" values. You will have a comprehensive understanding of the distribution of your various variables thanks to this function. Variables with categories and "value counts" Categorical variables may also be present in your dataset. These are discrete-valued variables that can be broken down into various groups or categories. For instance, the drive system is a categorical variable in our dataset and is divided into three categories: four-wheel drive, rear-wheel drive, and forward-wheel drive. The function "value counts" can be used to condense the categorical data. To make the column's name easier to read, we can alter it. It is clear that there are 118 front-wheel drive vehicles, 75 rear-wheel drive vehicles, and 8 four-wheel drive vehicles. Box Plots As you can see the different distributions of the data, box plots are a fantastic tool for visualizing numerical data. The median of the data, or where the center data point is, is what the box plot primarily reveals. The 75th percentile is indicated by the upper quartile, while the 25th percentile is indicated by the lower quartile. The interquartile range is represented by the data between the upper and lower quartiles. The lower and upper extremes come next. Above the 75th percentile, these are calculated as 1.5 times the interquartile range (IQR), and below the 25th percentile, as 1.5 times the IQR. Moreover, outliers are shown in box plots as distinct dots that appear outside of the top and lower extremities. You can quickly identify outliers as well as the distribution and skewness of the data with box plots. Comparing data between groups is simple with box plots. Scatter Plots Our data frequently contains continuous variables. The figures in these data points fall inside a certain range. For instance, price and engine size are continuous variables in our dataset. What if we wanted to know how engine size and cost relate to one another? Could the size of an engine indicate a car's price? Using a scatter plot is a nice method to see this. The scatter plot shows a point for each observation. The relationship between the two variables is depicted in this graphic. The variable you're using to forecast a result is known as the predictor variable. The engine size serves as our predictor variable in this situation. The variable you are attempting to forecast is known as the target variable. As the price would be the result in this scenario, it is our goal variable. The predictor variable is often placed on the x-axis, or horizontal axis, while the target variable is placed on the y-axis, or vertical axis, in a scatter plot. Conclusion In conclusion, one of the most important steps in the data analysis process is comprehending your data. You can study your data, get a summary of it, and better comprehend its fundamental characteristics by using descriptive statistics.