filenamea ='celltest.csv'
#Import necessary libraries
import pandas
from pandas import DataFrame, read_csv
import matplotlib.pyplot
import matplotlib
import seaborn
import math
# Enable inline plotting
%matplotlib inline
/home/main/anaconda2/envs/python3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment. warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.') /home/main/anaconda2/envs/python3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment. warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
def data(filenamea):
#import file
df1 = pandas.read_csv(filenamea) #import file
df1 = pandas.DataFrame(df1) #convert to dataframe
large = df1
#figure asthetics
biobots = ["#b30000","#e63131","#008081","#00d098","#e1e1e1"];
palette = seaborn.set_palette(biobots);
seaborn.set_context("paper");
seaborn.set_style('ticks')
fig, ax = matplotlib.pyplot.subplots()
#Set up boxplot
fig.set_size_inches(5, 4)
g= seaborn.boxplot(y=large.CellYieldFlask);
g.set(title='Cell Yield Per Flask');
matplotlib.pyplot.savefig("boxplot.png")
palette = seaborn.set_palette(biobots);
seaborn.set_context("paper");
# Set second figure
f,(ax1,ax2,ax3,ax4) = matplotlib.pyplot.subplots(ncols=4,figsize=(12,5));
seaborn.regplot(data=large,x="Passage",y="CellYieldFlask",ax=ax1,color="#e63131");
seaborn.regplot(data=large,x="DaysCultured",y="CellYieldFlask", color="#008081",ax=ax2);
seaborn.regplot(data=large,x="DensityPlated",y="CellYieldFlask", color="#00d098",ax=ax3);
seaborn.regplot(data=large,x="Flask",y="CellYieldFlask",color="#b30000",ax=ax4)
#ylabels
ax1.set(ylabel='Cell Yield Per Flask (Millions)')
ax2.set(ylabel='')
ax3.set(ylabel='')
ax4.set(ylabel='')
#xlabels
ax1.set(xlabel='Passage')
ax2.set(xlabel='Days Cultured')
ax3.set(xlabel='Cell Density Plated per cm^2')
ax4.set(xlabel= 'Flask Size')
#Titles
ax1.set(title='Cell Yield By Passage')
ax2.set(title='Cell Yield By Days Cultured')
ax3.set(title='Cell Yield By Density Plated')
ax4.set(title='Cell Yield by Flask Size')
matplotlib.pyplot.show()
f.savefig("trends.png")
#Data stats
data_matrix = large["CellYieldFlask"].describe()
data_matrix = pd.DataFrame(large["CellYieldFlask"].describe())
print(data_matrix)
def cultureanalysis(filenamea):
data(filenamea)
Images are saved as .png files and can be downloaded from the home page
cultureanalysis(filenamea)