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mdtables.py
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mdtables.py
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import numpy as np
import pandas as pd
import pytablewriter
def user_counts(df):
grps = df.UserRole.value_counts(dropna=False)
pct = [x / float(sum(grps)) for x in grps]
s1 = pd.Series(grps.values, index=grps.index, name='Count')
s2 = pd.Series(pct, index=grps.index, name='Total%')
s2 = s2.apply(lambda x: round(x*100, 2))
df_grp = pd.concat([s1, s2], axis=1)
return df_grp
def org_counts(df):
grps = df.OrganizationType.value_counts(dropna=False)
pct = [x / float(sum(grps)) for x in grps]
s1 = pd.Series(grps.values, index=grps.index, name='Count')
s2 = pd.Series(pct, index=grps.index, name='Total%')
s2 = s2.apply(lambda x: round(x*100, 2))
df_grp = pd.concat([s1, s2], axis=1)
return df_grp
def search_space_counts(col):
frames = []
clusters = [C1, C2, C3, C4]
for idx, cluster in enumerate(clusters):
grps = cluster[col].value_counts(dropna=False)
pct = [x / float(sum(grps)) for x in grps]
s1 = pd.Series(grps.values, index=grps.index, name='Count')
s2 = pd.Series(pct, index=grps.index, name='Total%')
s2 = s2.apply(lambda x: round(x*100, 2))
df_grp = pd.concat([s1, s2], axis=1)
df_grp =df_grp.reindex(['True', 'False', 'NA'])
frames.append(df_grp)
df_combo = pd.concat(frames, axis=1)
vals = df_combo.values
l1 = ['Cluster 1', 'Cluster 1', 'Cluster 2', 'Cluster 2', 'Cluster 3', 'Cluster 3', 'Cluster 4', 'Cluster 4']
l2 = ['Count', 'Total%', 'Count', 'Total%', 'Count', 'Total%', 'Count', 'Total%']
tuples = list(zip(l1, l2))
index = pd.MultiIndex.from_tuples(tuples, names=[col, ''])
df = pd.DataFrame(vals, index=['True', 'False', 'NA'], columns=index)
return df
def usersims_by_cluster(df):
pt = pd.pivot_table(df, values=['Created'], index=['User'], columns=['Cluster'], aggfunc='count', fill_value=0)
cluster_df = pd.DataFrame(pt.iloc[:, 0].index)
cluster_df['Cluster 1'] = pt.iloc[:, 0].values
cluster_df['Cluster 2'] = pt.iloc[:, 1].values
cluster_df['Cluster 3'] = pt.iloc[:, 2].values
cluster_df['Cluster 4'] = pt.iloc[:, 3].values
output = round(cluster_df.describe().loc[['mean', 'std', 'max']], 2)
return output
def write_mdtable(df):
'''Convert pandas dataframe to markdown'''
writer = pytablewriter.MarkdownTableWriter()
writer.table_name = "example_table"
writer.header_list = ['Index', 'Cluster 1: Count', 'Cluster 1: Total%', 'Cluster 2: Count', 'Cluster 2: Total%', 'Cluster 3: Count', 'Cluster 3: Total%', 'Cluster 4: Count', 'Cluster 4: Total%']
df.reset_index(level=0, inplace=True)
# writer.header_list = df.columns.tolist()
writer.value_matrix = df.values.tolist()
print(writer.write_table())
if __name__ == '__main__':
df = pd.read_pickle('data/df_clustered.pkl')
C1, C2, C3, C4 = df[df['Cluster'] == 1], df[df['Cluster'] == 2], df[df['Cluster'] == 3], df[df['Cluster'] == 4]
# user = user_counts(df)
# org = org_counts(df)
search_gen = search_space_counts('MultiGenSearch')
search_pv = search_space_counts('MultiPvSearch')
search_bat = search_space_counts('MultiBatSearch')
search_con = search_space_counts('MultiConSearch')
search_wind = search_space_counts('MultiWindSearch')
# sims_by_cluster = usersims_by_cluster(df)