<jupyter_start><jupyter_text>Analyze the data to see top N growing industries based on job postingsFocus on the entire data set to get a general idea of growth over 3 years. For more specific analysis could focus down on a certain time period.<jupyter_code>temp = df.groupby(['as_of_date','industry']).agg({'industry':"count"})
temp.columns = ['industry_count']
temp.reset_index(inplace=True)  
temp.as_of_date = pd.to_datetime(temp.as_of_date)
biotplot.figure.savefig('biotplot')
np.polyfit(range(len(temp[temp.industry == 'Aviation & Aerospace'].industry_count)), temp[temp.industry == 'Aviation & Aerospace'].industry_count, deg = 1)
a = temp.groupby('industry')
ind_col = []
slope_col = []
offset_col = []
for ind in temp.industry.unique():
    m, b = np.polyfit(range(len(temp[temp.industry == ind].industry_count)), temp[temp.industry == ind].industry_count, deg = 1)
    ind_col.append(ind)
    slope_col.append(m)
    offset_col.append(b)
inddf = pd.DataFrame()
inddf['industry'] = ind_col
inddf['slope'] = slope_col
inddf['offset'] = offset_col
inddf['scaled_offset'] = inddf['slope']/max(inddf.slope)
top10 = sorted(inddf.scaled_offset,reverse=True)[10]
inddf=inddf.sort_values(by=['scaled_offset'],ascending=False)
topindplot = inddf[inddf.scaled_offset >= top10].plot(x='industry',y='scaled_offset', kind='bar',title='Top 10 Growing Industries')
topindplot.figure.savefig('top10indplot')
biotplot = temp[temp.industry == 'Biotechnology'].plot(x = 'as_of_date', y='industry_count',title='Biotechnology')<jupyter_output><empty_output><jupyter_text>Looking at individual industries to see top hiring companies<jupyter_code>biodf = df[df.industry == 'Biotechnology']
biodf.as_of_date = pd.to_datetime(biodf.as_of_date)
bio = biodf.groupby(['company_name',pd.Grouper(key = 'as_of_date',freq='M')]).agg({'company_name':"count"})
bio.columns = ['company_count']
bio.reset_index(inplace=True)  
temp = bio[bio.as_of_date > '2018-01-01']

ind_col = []
slope_col = []
offset_col = []
for ind in temp.company_name.unique():
    if len(temp[temp.company_name == ind]) > 1:
        m, b = np.polyfit(range(len(temp[temp.company_name == ind].company_count)), temp[temp.company_name == ind].company_count, deg = 1)
    else:
        m, b = -100, 0
    ind_col.append(ind)
    slope_col.append(m)
    offset_col.append(b)
biocomp = pd.DataFrame()
biocomp['company'] = ind_col
biocomp['slope'] = slope_col
biocomp['offset'] = offset_col
bio[bio.company_name == 'Versartis, Inc.'].plot(x = 'as_of_date', y='company_count')<jupyter_output><empty_output>
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