Pymaceuticals, Inc.,
A new pharmaceutical company that specializes in anti-cancer medications. Recently, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
As a senior data analyst at the company,have been given access to the complete data from their most recent animal study. In this study, 249 mice who were identified with SCC tumors received treatment with a range of drug regimens. Over the course of 45 days, tumor development was observed and measured. The purpose of this study was to compare the performance of Pymaceuticals’ drug of interest, Capomulin, against the other treatment regimens.
The executive team has tasked you with generating all of the tables and figures needed for the technical report of the clinical study. They have also asked you for a top-level summary of the study results.
Task was broken down into the following tasks:
- Data
Prepration
- Generating summary
statistics
. - Creation of
bar
charts andpie
charts. - Calculation for
quartiles
, findoutliers
, and creation of abox plot
. - Creation for a
line plot
and ascatter plot
. - Calculation for
correlation
andregression
. - Final
analysis
.
Important
Key information users need to know to achieve their goal.
- Ran the required package dependency and data imports, and then merged the
mouse_metadata
andstudy_results
DataFrames into a single DataFrame.
- Displaied the number of ``unique
mice
IDs in the data, and then checked for any mouse ID with `duplicate` time points. Displaied the data associated with that mouse ID
- created a new
DataFrame
where this data isremoved
. Used thiscleaned DataFrame
for the remaining steps. - Displaied the updated number of unique
mice IDs
.
- Created a DataFrame of
summary statistics
. There Was more than one method to produce the results after. - Summary statistics are included:
- A row for each drug regimen. These regimen names have contained in the
index column
. - A column for each of the following statistics:
mean, median, variance, standard deviation, and SEM
of thetumor
volume.
- Generated two
bar
charts. - Both charts have identicals and the total total number of rows (Mouse ID/Timepoints) for each drug regimen throughout the study.
- Created the first bar chart with the
Pandas DataFrame.plot()
method.
- Created the second bar chart with
Matplotlib's pyplot methods
.
- Generated two
pie
charts. Both charts have identical and shown the distribution offemale
versusmale mice
in the study. - Created the first pie chart with the
Pandas DataFrame.plot()
method.
- Created the second pie chart with
Matplotlib's pyplot
methods.
- Calculated the final tumor volume of each mouse across four of the most promising
treatment
regimens: _Capomulin, Ramicane, Infubinol, and Ceftamin. _
- Then, calculated the
quartiles
andIQR
, and determined if there were anypotential outliers
across all four treatment regimens. - Used the following substeps:
- Created a
grouped DataFrame
that shows the last (greatest) time point for each mouse.Merged
this grouped DataFrame with the originalcleaned
DataFrame. - Created a list that holds the treatment names as well as a second, empty list to hold the tumor volume data.
Loopped
through each drug in the treatment list, locating the rows in the merged DataFrame that corresponds to each treatment.Appended
the resulting final tumor volumes for each drug to the empty list.- Determined
outliers
by using the upper and lower bounds, and then print the results.
- Used
Matplotlib
, generated abox plot
that shows the distribution of the final tumor volume for all the mice in each treatment group. Highlighted anypotential outliers
in the plot by changing theircolor and style
.
- Selected a single mouse that was treated with Capomulin, and generated a
line plot
of tumor volume versus time point for that mouse.
- Generated a
scatter plot
of mouse weight versusaverage
observed tumor volume for the entire Capomulintreatment
regimen.
- Calculated the
correlation coefficient
andlinear regression
model between mouse weight andaverage
observed tumor volume for the entire Capomulin treatment regimen. - Plotted the
linear regression
model on top of the previousscatter plot
.
Note
Useful information that users should know, even when skimming content.
- Created a new repository for this project called #
Matplotlib-Data-Visualization
, Cloned the new repository(remote) to local byterminal
. - Inside my local
Git repository
, created a folder for "Pymaceuticals
" - Added
Jupyter notebook
"(pymaceuticals_starter_Roshni.ipynb)" to this folder. This is the main script to run this analysis. - A Data folder that contains the
CSV
files(Raw Data) i have used. - Also this folder that contains "
pdf
" file that has the results from the conducted analysis. - Pushed these changes to
GitHub
profile bybash terminal
.