In [9]:

    

%pylab inline
import pandas as pd
import seaborn as sns
import datetime
from collections import OrderedDict
sns.set_context('talk', font_scale=1.5)
sns.set_style('white')


    

    




Populating the interactive namespace from numpy and matplotlib

In [10]:

    

xl = pd.ExcelFile('../BISC-104-Session01-Scientific-Method-Thursday-A.xlsx')

sheet_names = xl.sheet_names  # see all sheet names
sheet_names


    

    
Out[10]:





['TEMPLATE_DO_NOT_EDIT',
 'McKellan_Stanford_',
 'Adrian_Gabi',
 'Aviv_Ashley_Jiaxin',
 'Cynthia_Chenxi',
 'Alina_Cecil_Daniel']

In [11]:

    

all_df = OrderedDict()
master_df = pd.DataFrame()
for sheet_name in sheet_names[1:]:
    df = pd.read_excel('../BISC-104-Session01-Scientific-Method-Thursday-A.xlsx', sheet_name=sheet_name)
    df.columns = ['Day', 'Time',  '# Females', '# Males']
    all_df[sheet_name] = df
    master_df = pd.concat([master_df, df], ignore_index=True)


    

In [12]:

    

print(master_df.to_latex(index=False))


    

    




\begin{tabular}{llrr}
\toprule
Day &         Time &  \# Females &  \# Males \\
\midrule
  M &  13:15-13:30 &        9.0 &      9.0 \\
  T &  13:10-13:25 &       20.0 &      3.0 \\
  W &  13:25-13:40 &       12.0 &      7.0 \\
  T &  11:05-11:20 &       14.0 &      7.0 \\
  W &  09:30-09:45 &        7.0 &      4.0 \\
  T &  09:30-09:45 &       11.0 &      7.0 \\
  T &  12:00-12:30 &        9.0 &      7.0 \\
  T &  14:30-15:00 &       14.0 &     10.0 \\
  W &  15:00-15:30 &        NaN &      NaN \\
  W &  10:20-10:35 &        9.0 &      3.0 \\
\bottomrule
\end{tabular}

In [13]:

    

all_df = OrderedDict()
master_df = pd.DataFrame()
for sheet_name in sheet_names[1:]:
    df = pd.read_excel('../BISC-104-Session01-Scientific-Method-Thursday-A.xlsx', sheet_name=sheet_name)
    df.columns = ['Day', 'Time',  '# Females', '# Males']
    df['Group'] = sheet_name
    all_df[sheet_name] = df
    master_df = pd.concat([master_df, df], ignore_index=True)


    

In [14]:

    

master_df.columns


    

    
Out[14]:





Index(['Day', 'Time', '# Females', '# Males', 'Group'], dtype='object')

In [15]:

    

master_df_molten = pd.melt(master_df, id_vars=['Day', 'Time', 'Group'], value_vars=['# Females', '# Males'])
master_df_molten


    

    
Out[15]:







  

    

      

      
Day
      
Time
      
Group
      
variable
      
value
    
  
  

    

      
0
      
M
      
13:15-13:30
      
McKellan_Stanford_
      
# Females
      
9.0
    
    

      
1
      
T
      
13:10-13:25
      
Adrian_Gabi
      
# Females
      
20.0
    
    

      
2
      
W
      
13:25-13:40
      
Adrian_Gabi
      
# Females
      
12.0
    
    

      
3
      
T
      
11:05-11:20
      
Aviv_Ashley_Jiaxin
      
# Females
      
14.0
    
    

      
4
      
W
      
09:30-09:45
      
Aviv_Ashley_Jiaxin
      
# Females
      
7.0
    
    

      
5
      
T
      
09:30-09:45
      
Aviv_Ashley_Jiaxin
      
# Females
      
11.0
    
    

      
6
      
T
      
12:00-12:30
      
Cynthia_Chenxi
      
# Females
      
9.0
    
    

      
7
      
T
      
14:30-15:00
      
Cynthia_Chenxi
      
# Females
      
14.0
    
    

      
8
      
W
      
15:00-15:30
      
Cynthia_Chenxi
      
# Females
      
NaN
    
    

      
9
      
W
      
10:20-10:35
      
Alina_Cecil_Daniel
      
# Females
      
9.0
    
    

      
10
      
M
      
13:15-13:30
      
McKellan_Stanford_
      
# Males
      
9.0
    
    

      
11
      
T
      
13:10-13:25
      
Adrian_Gabi
      
# Males
      
3.0
    
    

      
12
      
W
      
13:25-13:40
      
Adrian_Gabi
      
# Males
      
7.0
    
    

      
13
      
T
      
11:05-11:20
      
Aviv_Ashley_Jiaxin
      
# Males
      
7.0
    
    

      
14
      
W
      
09:30-09:45
      
Aviv_Ashley_Jiaxin
      
# Males
      
4.0
    
    

      
15
      
T
      
09:30-09:45
      
Aviv_Ashley_Jiaxin
      
# Males
      
7.0
    
    

      
16
      
T
      
12:00-12:30
      
Cynthia_Chenxi
      
# Males
      
7.0
    
    

      
17
      
T
      
14:30-15:00
      
Cynthia_Chenxi
      
# Males
      
10.0
    
    

      
18
      
W
      
15:00-15:30
      
Cynthia_Chenxi
      
# Males
      
NaN
    
    

      
19
      
W
      
10:20-10:35
      
Alina_Cecil_Daniel
      
# Males
      
3.0
    
  

In [16]:

    

fig, ax = plt.subplots(figsize=(12, 10))
sns.barplot(data=master_df_molten, x='Group', y='value', hue='variable')
plt.xticks(rotation=45)
ax.legend(frameon=False)
fig.tight_layout()

fig.savefig('BISC104_Th_A_bar.pdf')


    

In [18]:

    

all_df = OrderedDict()
master_df = pd.DataFrame()
for sheet_name in sheet_names[1:]:
    df = pd.read_excel('../BISC-104-Session01-Scientific-Method-Thursday-A.xlsx', sheet_name=sheet_name)
    df.columns = ['Day', 'Time',  '# Females', '# Males']
    df['Group'] = sheet_name
    df[['Time_start', 'Time_end']] = df['Time'].str.split('-', n=1, expand=True)
    df['Time_start'] = pd.to_datetime(df['Time_start'])#.astype(pd.Timestamp)
    df['Time_end'] = pd.to_datetime(df['Time_end'])#.astype(pd.Timestamp)
    df['delta'] = [datetime.timedelta.total_seconds(x) for x in df['Time_end'] - df['Time_start']]
    all_df[sheet_name] = df
    df['# Females'] = df['# Females']/df['delta'].astype(float)
    df['# Males'] = df['# Males']/df['delta']
    master_df = pd.concat([master_df, df], ignore_index=True)


    

In [ ]:

    

df['delta']


    

In [19]:

    

master_df_molten = pd.melt(master_df, id_vars=['Day', 'Time', 'Group'], value_vars=['# Females', '# Males'])
fig, ax = plt.subplots(figsize=(12, 10))
sns.barplot(data=master_df_molten, x='Group', y='value', hue='variable')
plt.xticks(rotation=45)
ax.set_ylabel('#/second')
fig.tight_layout()
ax.legend(frameon=False)
fig.savefig('BISC104_Th_A_bar_normalized.pdf')


    

In [20]:

    

all_df = OrderedDict()
master_df = pd.DataFrame()
for sheet_name in sheet_names[1:]:
    df = pd.read_excel('../BISC-104-Session01-Scientific-Method-Thursday-A.xlsx', sheet_name=sheet_name)
    df.columns = ['Day', 'Time',  '# Females', '# Males']
    df['Group'] = sheet_name
    df[['Time_start', 'Time_end']] = df['Time'].str.split('-', n=1, expand=True)
    df['Time_start'] = pd.to_datetime(df['Time_start'])#.astype(pd.Timestamp)
    df['Time_end'] = pd.to_datetime(df['Time_end'])#.astype(pd.Timestamp)
    df['delta'] = [datetime.timedelta.total_seconds(x) for x in df['Time_end'] - df['Time_start']]
    all_df[sheet_name] = df
    #df['# Females'] = df['# Females']/df['delta'].astype(float)
    #df['# Males'] = df['# Males']/df['delta']
    master_df = pd.concat([master_df, df], ignore_index=True)
master_df_molten = pd.melt(master_df, id_vars=['Day', 'Time', 'Group', 'delta'], value_vars=['# Females', '# Males'])

sns.lmplot(x="delta", y="value", hue="variable", data=master_df_molten, legend_out=True)

plt.xlabel('Time duration (s)')
plt.ylabel('Count')
plt.tight_layout()
plt.savefig('BISC104_Th_A_bar_timewise.pdf')


    

In [ ]:

    

master_df_molten


    

In [ ]: