Download the list with the date value in pandas data and the site operation over time

Question

I have some data on Twitter that I would like to describe overtime work based on the type of tweets (tweet / mention / retweet).

Data is currently being loaded into a tuple list containing date and type :

time = [('2014-04-13', 'tweet'),
        ('2014-04-13', 'tweet'),
        ('2014-04-13', 'mention'),
        ('2014-04-13', 'retweet'),
        ('2014-04-13', 'mention'),
        ('2014-04-13', 'tweet'),
        ('2014-04-13', 'retweet'),
        ('2014-04-13', 'mention'),
        ('2014-04-13', 'tweet'),
        ('2014-04-13', 'retweet'),
        ('2014-04-13', 'retweet'),
        ('2014-04-13', 'mention'),
        ('2014-04-13', 'tweet'),
        ('2014-04-13', 'tweet'),
        ('2014-04-13', 'tweet'),
        ('2014-04-13', 'tweet'),
        ('2014-04-13', 'mention'),
        ('2014-04-13', 'retweet'),
        ('2014-04-13', 'mention'),
        ('2014-04-13', 'tweet')]

I loaded the data into a pandas DataFrame:

time_df = pd.DataFrame(time, columns=['date','time'])

Now that the data looks like this:

         date     time
0  2014-04-13    tweet
1  2014-04-13    tweet
2  2014-04-13  mention
3  2014-04-13  retweet
4  2014-04-13  mention
...
...
...

However, now I'm lost when it comes to building this data over time. In addition, I would like to split each type (tweet / mention / retweet) as a different color line. It should also be noted that sometimes I may need to aggregate data by day / week / month.

, , , Tweet, Mention, Retweet:

Answer 1

, , , , .

:

import numpy as np
import pandas
import random

tweet_types = ['tweet', 'retweet', 'mention']
index = pandas.DatetimeIndex(freq='5min', start='2014-04-13', end='2014-05-13')
tweets = [random.choice(tweet_types) for _ in range(len(index))]
time_df = pandas.DataFrame(index=index, data=tweets, columns=['tweet type'])
time_df['day'] = time_df.index.date
time_df['count'] = 1
print(time_df.head())

, :

                     tweet type         day  count
2014-04-13 00:00:00     mention  2014-04-13      1
2014-04-13 00:05:00     mention  2014-04-13      1
2014-04-13 00:10:00       tweet  2014-04-13      1
2014-04-13 00:15:00       tweet  2014-04-13      1
2014-04-13 00:20:00     retweet  2014-04-13      1

count, - , :

daily_counts = time_df.groupby(by=['tweet type', 'day']).count()
daily_counts_xtab = daily_counts.unstack(level='tweet type')['count']
print(daily_counts_xtab.head())

...

tweet type  mention  retweet  tweet
day                                
2014-04-13       89      101     98
2014-04-14       98      113     77
2014-04-15       87      103     98
2014-04-16       81      107    100
2014-04-17       96       92    100

,

daily_counts_xtab.plot()

: