<jupyter_start><jupyter_text>Sum of Targets<jupyter_code>ax = train_targets_scored.sum(axis = 0).iloc[1:].plot.bar(figsize=(20,10), title = "Sum of Targets")
for i, t in enumerate(ax.get_xticklabels()):
    if (i % 5) != 0:
        t.set_visible(False)
ax.set_ylabel("Sum")
ax.tick_params(axis='y', labelsize=14)
ax.tick_params(axis='x', labelsize=14)
plt.savefig('Sum_Of_Targets.pdf')<jupyter_output><empty_output><jupyter_text>General Distribtuion<jupyter_code>Genes = train_features.iloc[:,4:775] #Only Gene Values
Cells = train_features.iloc[:,776:876] #Only Cell Values
GeneSum = np.sum(Genes, axis = 1)#Engineer features
CellSum = np.sum(Cells, axis = 1)
Genemax = np.max(Genes, axis = 1)
Cellmax = np.max(Cells, axis = 1)
Genemin = np.min(Genes, axis = 1)
Cellmin = np.min(Cells, axis = 1)
Genemean = np.mean(Genes, axis = 1)
Cellmean = np.average(Cells, axis = 1)
Genestd = np.std(Genes, axis = 1)
Cellstd = np.std(Cells, axis = 1)
Cellstd = pd.DataFrame(data=Cellstd, columns=['Cell Value'])
Cellstd.plot.hist( bins = 30,title = "Plot of Std of Cells")
plt.savefig('Plot of Std of Cells.pdf')
Cellmean = pd.DataFrame(data=Cellmean, columns=['Cell Value'])
Cellmean.plot.hist(bins = 30, title = "Plot of Mean of Cells")
plt.savefig('Plot of Mean of Cells.pdf')
Cellmin = pd.DataFrame(data=Cellmin, columns=['Cell Value'])
Cellmin.plot.hist( bins = 30,title = "Histogram Plot of Min of Cells")
plt.savefig('Plot of Min of Cells.pdf')
Cellmax = pd.DataFrame(data=Cellmax, columns=['Cell Value'])
Cellmax.plot.hist( bins = 30,title = "Histogram Plot of Max of Cells")
plt.savefig('Plot of Max of Cells.pdf')
CellSum = pd.DataFrame(data=CellSum, columns=['Cell Value'])
CellSum.plot.hist( bins = 30,title = "Histogram Plot of Sum of Cells")
plt.savefig('Plot of Sum of Cells.pdf')
GeneSum = pd.DataFrame(data=GeneSum, columns=['Gene Value'])
GeneSum.plot.hist( bins = 30,title = "Histogram Plot of Sum of Genes")
plt.savefig('Plot of Sum of Genes.pdf')
Genemean = pd.DataFrame(data=Genemean, columns=['Gene Value'])
Genemean.plot.hist( bins = 30,title = "Histogram Plot of Mean of Genes")
plt.savefig('Plot of Mean of Genes.pdf')
Genemin = pd.DataFrame(data=Genemin, columns=['Gene Value'])
Genemin.plot.hist( bins = 30,title = "Histogram Plot of Min of Genes")
plt.savefig('Plot of Min of Genes.pdf')
Genemax = pd.DataFrame(data=Genemax, columns=['Gene Value'])
Genemax.plot.hist( bins = 30,title = "Histogram Plot of Max of Genes")
plt.savefig('Plot of Max of Genes.pdf')
Cellmean = np.average(Cells, axis = 1)
for i in range(0,len(Cellmean)): #find id's with low means so they can be visualize in next steps
    if Cellmean[i] < -9.8:
        print(i)<jupyter_output>1021
7034
9588
10399
18553
20508<jupyter_text>Individual Plots<jupyter_code># gene_expression values for 1st sample
train_features.iloc[0, 4:4+772].plot()
plt.title('Gene Expressions (sample 0)')
plt.savefig('GeneExpressions0.pdf')
plt.show()


# Sorted gene_feature values for 1st sample
train_features.iloc[0, 4:4+772].sort_values().plot()
plt.title('Gene Expressions (Sorted by Values) (sample 0)')
plt.savefig('GeneExpressions0Sorted.pdf')
#plt.show()

# Checking progression of gene_expressions values

# gene_expression values for 6th sample
train_features.iloc[6, 4:4+772].plot()
plt.title('Gene Expressions (6)')
plt.savefig('GeneExpressionscsd6.pdf')
plt.show()

# Sorted gene_feature values for 6th sample
train_features.iloc[6, 4:4+772].sort_values().plot()
plt.title('Gene Expressions (Sorted by Values) (6)')
plt.savefig('GeneExpressions6Sorted.pdf')
plt.show()
# Checking progression of gene_expressions values

# gene_expression values for 55th sample
train_features.iloc[55, 4:4+772].plot()
plt.title('Gene Expressions (55)')
plt.savefig('GeneExpressions55.pdf')
plt.show()

# Sorted gene_feature values for 55th sample
train_features.iloc[55, 4:4+772].sort_values().plot()
plt.title('Gene Expressions (Sorted by Values) (55)')
plt.savefig('GeneExpressions55Sorted.pdf')
plt.show()
# Checking progression of gene_expressions values

# gene_expression values for 1021st sample
train_features.iloc[1021, 4:4+772].plot()
plt.title('Gene Expressions (1021)')
plt.savefig('GeneExpressions1021.pdf')
plt.show()

# Sorted gene_feature values for 1021st sample
train_features.iloc[1021, 4:4+772].sort_values().plot()
plt.title('Gene Expressions (Sorted by Values) (1021)')
plt.savefig('GeneExpressions1021Sorted.pdf')
plt.show()
# Checking progression of cell viability values

# cell viability values for 1st sample
train_features.iloc[0, 4+772:].plot()
plt.title('Cell Viability (0)')
plt.savefig('CellViability1.pdf')
plt.show()

# Sorted gene_feature values for 1st sample
train_features.iloc[0, 4+772:].sort_values().plot()
plt.title('Cell Viability (Sorted by Values) (0)')
plt.savefig('CellViability1Sorted.pdf')
plt.show()
# Checking progression of cell viability values

# cell viability values for 6th sample
train_features.iloc[6, 4+772:].plot()
plt.title('Cell Viability (6)')
plt.savefig('CellViability6.pdf')
plt.show()

# Sorted gene_feature values for 6th sample
train_features.iloc[6, 4+772:].sort_values().plot()
plt.title('Cell Viability (Sorted by Values) (6)')
plt.savefig('CellViability6Sorted.pdf')
plt.show()
# Checking progression of cell viability values

# cell viability values for 55th sample
train_features.iloc[55, 4+772:].plot()
plt.title('Cell Viability (55)')
plt.savefig('CellViability55.pdf')
plt.show()

# Sorted gene_feature values for 55th sample
train_features.iloc[55, 4+772:].sort_values().plot()
plt.title('Cell Viability (Sorted by Values) (55)')
plt.savefig('CellViability55Sorted.pdf')
plt.show()
# Checking progression of cell viability values

# cell viability values for 1021st sample
train_features.iloc[1021, 4+772:].plot()
plt.title('Cell Viability (1021)')
plt.savefig('CellViability1021.pdf')
plt.show()

# Sorted gene_feature values for 1021st sample
train_features.iloc[1021, 4+772:].sort_values().plot()
plt.title('Cell Viability (Sorted by Values) (1021)')
plt.savefig('CellViability1021Sorted.pdf')
plt.show()<jupyter_output><empty_output><jupyter_text>Distribution Plots<jupyter_code>import seaborn as sns
sns.distplot(train_features.loc[:, 'g-1']).set_title("Distribution of G-1")
plt.savefig('G-1Distribution.pdf')
sns.distplot(train_features.loc[:, 'g-80']).set_title("Distribution of G-80")
plt.savefig('G-80Distribution.pdf')
sns.distplot(train_features.loc[:, 'c-1']).set_title("Distribution of C-1")
plt.savefig('C-1Distribution.pdf')
sns.distplot(train_features.loc[:, 'c-80']).set_title("Distribution of C-80")
plt.savefig('C-80Distribution.pdf')<jupyter_output><empty_output>
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