There is not a very big difference between the two, but both have different purposes.Įxploratory Data Analysis(EDA): Exploratory data analysis is a complement to inferential statistics, which tends to be fairly rigid with rules and formulas. While starting a career in Data Science, people generally don’t know the difference between Data analysis and exploratory data analysis. sns.Exploratory data analysis is one of the best practices used in data science today. Whereas a fewer difference may attract more sellers.įrom the below graph, you can observe that the volume is high for smaller values of open-high as compared to larger values of open-high. As there is no information about the number of buyers and sellers, we can only guess that a high difference in opening and highest value of the stock might attract more buyers. This means that the greater the difference between the opening value of the stock and the highest value of stock that day, the greater will be the stocks traded that day. The open-high shows the most negative correlation with the volume feature. Correlation between those features and the volume feature will tell us how a change in that feature impacts the number of stocks traded that day. Variables having a large correlation value with volume represent that those numbers might have intrigued a large number of buyers and sellers. The main variable we are going to focus on in this dataset is the ‘Volume’. Understanding these numbers and finding the pattern is an art, and we the data analysts are the artists.

Based on the correlation values only we can derive several insights for this dataset. Sns.heatmap(rr(),cmap='Blues',annot=True)ĭoes this heatmap look more informative than the previous one? I hope you think that too. Now we know a little about heatmaps, we can proceed towards creating the heatmap on our UBER stocks dataset using the seaborn library. Or simply, we can say whenever x increases, y increases too. When the correlation between x and y is 1, that means x has a positive correlation with y.It means that x and y have no correlation and are independent. When the correlation between x and y is 0.The more the value is close to -1, the more will be the negative correlation. It simply means when the value of x increases, the value of y would decrease. When the correlation between x and y is -1, that means x has a negative correlation with y.Heatmap – A heatmap is a 2d representation of data that tells how much a variable is correlated to other variables. You can get more information about the data set variables by plotting a heat map. You can plot the original data frame to see how a dataset without null values would look like. The above image shows that the volume column contains around 25 null values.

I, Eda by Howard R. Price