Sort strings in column and print graph

Question

I have dataframe, but all strings are duplicated and when I try print the graph, It contain duplicated column. I try to delete it, but then my graph print incorrectly. My csv is

Answer 1

You can first add new column sort in function f, then sorted values by column pair of websites and last drop_duplicates by columns used_at and sort:

import pandas as pd
import itertools

df = pd.read_csv("avito_trend.csv", 
                      parse_dates=[2])


def f(df):
    dfs = []
    i = 0
    for x in [list(x) for x in itertools.combinations(df['address'].unique(), 2)]:
        i += 1
        c1 = df.loc[df['address'].isin([x[0]]), 'ID']
        c2 = df.loc[df['address'].isin([x[1]]), 'ID']
        c = pd.Series(list(set(c1).intersection(set(c2))))
        #add inverted intersection c2 vs c1
        c_invert = pd.Series(list(set(c2).intersection(set(c1))))
        dfs.append(pd.DataFrame({'common users':len(c), 'pair of websites':' and '.join(x), 'sort': i}, index=[0]))
        #swap values in x
        x[1],x[0] = x[0],x[1]
        dfs.append(pd.DataFrame({'common users':len(c_invert), 'pair of websites':' and '.join(x), 'sort': i}, index=[0]))
    return pd.concat(dfs)

common_users = df.groupby([df['used_at'].dt.year]).apply(f).reset_index(drop=True, level=1).reset_index()

common_users = common_users.sort_values('pair of websites')
common_users = common_users.drop_duplicates(subset=['used_at','sort']) 
#print common_users

graph_by_common_users = common_users.pivot(index='pair of websites', columns='used_at', values='common users')
#print graph_by_common_users

#change order of columns
graph_by_common_users = graph_by_common_users[[2015,2014]]
graph_by_common_users = graph_by_common_users.sort_values(2014, ascending=False)

ax = graph_by_common_users.plot(kind='barh', width=0.5, figsize=(10,20))
[label.set_rotation(25) for label in ax.get_xticklabels()]

rects = ax.patches 
labels = [int(round(graph_by_common_users.loc[i, y])) for y in graph_by_common_users.columns.tolist() for i in graph_by_common_users.index] 
for rect, label in zip(rects, labels): 
    height = rect.get_height() 
    ax.text(rect.get_width() + 20, rect.get_y() - 0.25 + rect.get_height(), label, fontsize=8) 

#sorting values of legend
handles, labels = ax.get_legend_handles_labels()
# sort both labels and handles by labels
labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
ax.legend(handles, labels)     

My graph:

EDIT:

Comment is:

Why did you creare c_invert and x1,x[0] = x[0],x1

Because combinations for years 2014 and 2015 were different - 4 values were missing in first and 4 in second column:

used_at                                2015    2014
pair of websites                                   
avito.ru and drom.ru                 1491.0  1716.0
avito.ru and auto.ru                 1473.0  1602.0
avito.ru and e1.ru                   1153.0  1364.0
drom.ru and auto.ru                     NaN   874.0
e1.ru and drom.ru                     539.0   634.0
avito.ru and irr.ru/cars              403.0   602.0
avito.ru and am.ru                    262.0   579.0
e1.ru and auto.ru                     451.0   475.0
avito.ru and cars.mail.ru/sale        256.0   424.0
drom.ru and irr.ru/cars               277.0   423.0
auto.ru and irr.ru/cars               288.0   409.0
auto.ru and am.ru                     224.0   408.0
drom.ru and am.ru                     187.0   394.0
auto.ru and cars.mail.ru/sale         195.0   330.0
avito.ru and avtomarket.ru            205.0   299.0
drom.ru and cars.mail.ru/sale         189.0   292.0
drom.ru and avtomarket.ru             175.0   247.0
auto.ru and avtomarket.ru             162.0   243.0
e1.ru and irr.ru/cars                 148.0   235.0
e1.ru and am.ru                        99.0   224.0
am.ru and irr.ru/cars                   NaN   223.0
irr.ru/cars and cars.mail.ru/sale      94.0   197.0
am.ru and cars.mail.ru/sale             NaN   166.0
e1.ru and cars.mail.ru/sale           105.0   154.0
e1.ru and avtomarket.ru               105.0   139.0
avtomarket.ru and irr.ru/cars           NaN   139.0
avtomarket.ru and am.ru                72.0   133.0
avtomarket.ru and cars.mail.ru/sale    48.0   105.0
auto.ru and drom.ru                   799.0     NaN
cars.mail.ru/sale and am.ru            73.0     NaN
irr.ru/cars and am.ru                 102.0     NaN
irr.ru/cars and avtomarket.ru          73.0     NaN

Then I create all inverted combination - problem was solved. But why there are NaN? Why combinations are different in 2014 and 2015?

I add to function f:

def f(df):
    print df['address'].unique()

    dfs = []
    i = 0
    for x in [list(x) for x in itertools.combinations((df['address'].unique()), 2)]:
...
...

and output was (why first print twice is described in warning here ):

['avito.ru' 'e1.ru' 'drom.ru' 'auto.ru' 'avtomarket.ru' 'am.ru'
 'irr.ru/cars' 'cars.mail.ru/sale']
['avito.ru' 'e1.ru' 'drom.ru' 'auto.ru' 'avtomarket.ru' 'am.ru'
 'irr.ru/cars' 'cars.mail.ru/sale']
['avito.ru' 'e1.ru' 'auto.ru' 'drom.ru' 'irr.ru/cars' 'avtomarket.ru'
 'cars.mail.ru/sale' 'am.ru']

So lists are different and then combinations are different too -> I get some NaN values.

Solution is sorting list of combinations.

def f(df):
    #print (sorted(df['address'].unique()))   
    dfs = []
    for x in [list(x) for x in itertools.combinations(sorted(df['address'].unique()), 2)]:
        c1 = df.loc[df['address'].isin([x[0]]), 'ID']
        ...
        ...

All code is:

import pandas as pd
import itertools

df = pd.read_csv("avito_trend.csv", 
                      parse_dates=[2])

def f(df):
    #print (sorted(df['address'].unique()))   
    dfs = []
    for x in [list(x) for x in itertools.combinations(sorted(df['address'].unique()), 2)]:
        c1 = df.loc[df['address'].isin([x[0]]), 'ID']
        c2 = df.loc[df['address'].isin([x[1]]), 'ID']
        c = pd.Series(list(set(c1).intersection(set(c2))))
        dfs.append(pd.DataFrame({'common users':len(c), 'pair of websites':' and '.join(x)}, index=[0]))
    return pd.concat(dfs)

common_users = df.groupby([df['used_at'].dt.year]).apply(f).reset_index(drop=True, level=1).reset_index()
#print common_users

graph_by_common_users = common_users.pivot(index='pair of websites', columns='used_at', values='common users')

#change order of columns
graph_by_common_users = graph_by_common_users[[2015,2014]]
graph_by_common_users = graph_by_common_users.sort_values(2014, ascending=False)
#print graph_by_common_users

ax = graph_by_common_users.plot(kind='barh', width=0.5, figsize=(10,20))
[label.set_rotation(25) for label in ax.get_xticklabels()]

rects = ax.patches 
labels = [int(round(graph_by_common_users.loc[i, y])) \
for y in graph_by_common_users.columns.tolist() \
for i in graph_by_common_users.index]

for rect, label in zip(rects, labels): 
    height = rect.get_height() 
    ax.text(rect.get_width()+20, rect.get_y() - 0.25 + rect.get_height(), label, fontsize=8)

    handles, labels = ax.get_legend_handles_labels()
    # sort both labels and handles by labels
    labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
    ax.legend(handles, labels)   

And graph:

Answer 2

DataFrame Setup Issues

It looks like your DataFrame is not structure the way you would like it to be. Your DataFrame contains 2014 and 2015 as column header names not as row values on the used_at index. Also used_at is the index name not the index label of the first row.

You can test that this is true by executing:

import pandas as pd
from cStringIO import StringIO

text_data = '''
used_at            2014  2015
address                      
am.ru               621   273
auto.ru            1752  1595
avito.ru           5460  4631
avtomarket.ru       314   215
cars.mail.ru/sale   457   271
drom.ru            1934  1623
e1.ru              1654  1359
irr.ru/cars         619   426
'''

# Read in tabular data with used_at row as header
df = pd.read_table(StringIO(text_data), sep='\s+', index_col=0)
print 'DataFrame created with used_at row as header:'
print df
print 

# print df.used_at would cause AttributeError: 'DataFrame' object has no attribute 'used_at'
print 'df columns    :', df.columns
print 'df index name :', df.index.name
print

---

DataFrame created with used_at row as header:
                   2014  2015
used_at                      
address             NaN   NaN
am.ru               621   273
auto.ru            1752  1595
avito.ru           5460  4631
avtomarket.ru       314   215
cars.mail.ru/sale   457   271
drom.ru            1934  1623
e1.ru              1654  1359
irr.ru/cars         619   426

df columns    : Index([u'2014', u'2015'], dtype='object')
df index name : used_at