In summary statistics, we typically use two types of statistics to demonstrate the pattern of the data. One is the central tendency and the other is variation. For central tendency, we usually use the mean to represent the overall pattern of the data. Sometimes you may also include the median windows of spec to outliers. Mode which is the most frequency occurred value in the verbal, its occasional used as well. For variation, there are three types of statistics that measure the variation of the data. Variance, standard deviation, and coefficient of variation. Variance system average square distance of each individual data point to the mean. It is hard to interpret and we typically use standard deviation, which is the square root of the variance, and therefore measure the average distance of each individual data point to the mean. Occasionally, we will use the coefficient of variation to measure variation as well. Coefficient of variation is defined as the standard deviation divided by the mean. It is always between zero and one and therefore it is useful when we are comparing the variations of variables of different measurements scale. In Jupyter notebook, we can use the describe function to calculate the summary statistics of all the numerical variable. This function will return the number of observation they count hear, the mean, standard deviation, median, minimum and max and, a first third quartiles of the values of the variable. We can also add non-numerical variable, introduce summary statistics table by adding the argument include equals to all. You can see that for the non-numerical variable, the qualitative variable. This table provides the number of distinct values in the variable, the unique. It also provides the most frequently appear value of the variable, as well as its frequency, the top and freq. If we are only trying to calculate summary statistics of a single variable, we can simply specify the variable name and use the describe function. If you're only trying to find out for example, the mean, the median, a standard deviation of a single variable, we can simply use the mean function. The median function and the standard deviation, abbreviated as std function, to find the standard deviation of a single variable. We can also calculate the summary statistics of variable based on the value of another variable using the groupby function. In this line of code, we are trying to find the mean of other numerical variables based on whether the team won or lost a game. We first specify dataframe NBA games, and we'll use the group by function based on whether the team won lost again. This would be based on the variable WL we'll use a bracket to specify the variable WL. Then we'll use the mean function to calculate the mean of all the other numerical variables. Similarly, we can calculate the mean of a single variable conditioned on the value of another variable. The describe function can also provide important information about a date variable. For the game date, we can see that the describe function returns the number of unique value in the game date variable. It means that the number of unit value here is 1,559. It means that there are games that happen in 1,559 different days. He also returns the most frequently appear date it was on April 15, that means that the date with the most number of games. He also shows the first and the last date of our data set. Basic summary and descriptive analysis often involve visualizing the distribution of the data as well. We can visualize the distribution of a variable by creating a histogram. Let's create a histogram of the point variable PTS which is the point earned by each team. We can use the H-I-S-T function, the hist function in Python to graph our basic histogram. Inside the function we'll specify the variable name using the column equals two variable name. In histogram, the horizontal axis specified the value of the variable, and the vertical axis indicates the frequency that this value occurs. We can see that most of the values of the point variable are between 80-120. We can also change the number of bins in a histogram. Let's say we want to specify 20 bins, which means that we will first take the difference between the maximum value and the minimum value of the point and divide it by 20 to find the actual interval size of the histogram. We can add the bins equals to 20 option inside the hist function. Sometimes for visual appeal, we may also want to leave some space between the columns in the histogram. In this case, we can simply narrow the width of the column. We can specify our width equals 0.9, which means that we won 90 percent after width rather than a 100 percent. Finally, let's save our updated NBA games dataset. Let's call it NBA_Games2. Again, we want to export this data set as a CSV file using this 2_CSV function in Python. We will use index equals to false, so that we don't add additional row index number into the CSV file.
