Data-Visualization-Challenge

Prepare the data.

• using the start code

• import the data and use the method "loc" "len" "groupby" to clear the data

Generate summary statistics.

Create bar charts and pie charts.

• based on clear data using different methods: pandas and pyplot to generate bar chart

• using "value_counts" and creating list of female and male mice to create pie chart in pandas and pyplot

Calculate quartiles, find outliers, and create a box plot.

• Put treatments into a list for for loop (and later for plot labels) [Capomulin, Ramicane, Infubinol, Ceftamin]

• get the data of tumor volume, drug regimen, mouse id and reshape the form of the data

• Create empty list to fill with tumor vol data (for plotting)

• Locate the rows which contain mice on each drug and get the tumor volumes by loop and using the formula of quartiles to get quartile and outliers.

• using the method boxplot to create box plot

Create a line plot and a scatter plot

• sort out mouse ID l509

• make sure what put on x_axis is timepoint , y_axis is tumor volume

• using the method "plt.plot" and "plt.scatter"

• Calculate correlation and regression using "correlation = st.pearsonr(x,y)" "(slope, intercept, rvalue, pvalue, stderr) = linregress(x,y)" "regress_values = x['Weight (g)'] * slope + intercept" "line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2)) "

Write down final analysis in the head of the file.