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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Diving into Flickr data with python\n",
"\n",
"This is a work in progress"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import flickrapi\n",
"import pandas as pd\n",
"import json"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 108
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Prerequisite:**\n",
"\n",
"In order to understand this tutorial you need knowledge about a few things:\n",
"- python and numpy basic knowledge\n",
"- what is the json format\n",
"- what is an api\n",
"- some basic knowledge about pandas. The official doc has a '10 minutes to pandas' section that should do. \n",
"\n",
"Happy googling! :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## I. Learning some basic operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we declare a flickrapi object with our flickr API key"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"api_key = ''\n",
"flickr = flickrapi.FlickrAPI(api_key, format='json')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 109
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we retrieve the faves (favorites) of a selected photo.\n",
"\n",
"Here we choose to use the JSON format because it is readable by pandas."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fav = flickr.photos_getFavorites(photo_id='14368887810');"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By printing the content of the string, we see that some characters have to be removed for having a valid JSON string"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'head of fav: ',fav[:50]\n",
"print 'tail of fav: ',fav[-50:],'\\n'\n",
"# to return a valid json string, just remove 14 first characters and the last one\n",
"print 'head of fav (valid): ',fav[14:50]\n",
"print 'tail of fav (valid): ',fav[-50:-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"head of fav: jsonFlickrApi({\"photo\":{\"person\":[{\"nsid\":\"1208257\n",
"tailof fav: s\":12, \"perpage\":10, \"total\":\"120\"}, \"stat\":\"ok\"}) \n",
"\n",
"head of fav (valid): {\"photo\":{\"person\":[{\"nsid\":\"1208257\n",
"tail of fav (valid): s\":12, \"perpage\":10, \"total\":\"120\"}, \"stat\":\"ok\"}\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We parse JSON string, basically what it does is transforming the string into a dict"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s_fav = json.loads(fav[14:-1])\n",
"print str(s_fav)[:200] # printing only the beginning"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'photo': {u'perpage': 10, u'farm': 3, u'pages': 12, u'id': u'14368887810', u'person': [{u'username': u'Lukasan2014', u'family': 0, u'realname': u'', u'nsid': u'120825773@N05', u'iconserver': u'3908'\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the content related to favorites is contained in the entry 'person' in the form of a list"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print str(s_fav['photo']['person'])[:200], '\\n' # display the fav list\n",
"print s_fav['photo']['person'][5], '\\n' # display info about one fav\n",
"print s_fav['photo']['person'][5]['favedate'], '\\n' # display fav date (UNIX timestamp)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[{u'username': u'Lukasan2014', u'family': 0, u'realname': u'', u'nsid': u'120825773@N05', u'iconserver': u'3908', u'favedate': u'1406519587', u'contact': 0, u'friend': 0, u'iconfarm': 4}, {u'username' \n",
"\n",
"{u'username': u'christilou1', u'family': 0, u'realname': u'', u'nsid': u'38030824@N04', u'iconserver': u'4108', u'favedate': u'1406116570', u'contact': 0, u'friend': 0, u'iconfarm': 5} \n",
"\n",
"1406116570 \n",
"\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can import the information into a pandas data frame"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# read only valid json sub string into data frame\n",
"df_fav = pd.read_json(json.dumps(s_fav['photo']['person']));"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can check we have a data frame that contains only information about favorites"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_fav"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>contact</th>\n",
" <th>family</th>\n",
" <th>favedate</th>\n",
" <th>friend</th>\n",
" <th>iconfarm</th>\n",
" <th>iconserver</th>\n",
" <th>nsid</th>\n",
" <th>realname</th>\n",
" <th>username</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1406519587</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 3908</td>\n",
" <td> 120825773@N05</td>\n",
" <td> </td>\n",
" <td> Lukasan2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1406309214</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 2927</td>\n",
" <td> 91007561@N05</td>\n",
" <td> </td>\n",
" <td> C\u01b0\u1eddng Euro | Photography</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1406293554</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 3673</td>\n",
" <td> 104486340@N03</td>\n",
" <td> JM Fikko</td>\n",
" <td> jm.fikko</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1406259611</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 2939</td>\n",
" <td> 43738607@N07</td>\n",
" <td> </td>\n",
" <td> .brianday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1406239666</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 3679</td>\n",
" <td> 60142567@N05</td>\n",
" <td> </td>\n",
" <td> Paxtorino</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1406116570</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" <td> 4108</td>\n",
" <td> 38030824@N04</td>\n",
" <td> </td>\n",
" <td> christilou1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1405960894</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 2907</td>\n",
" <td> 96274428@N05</td>\n",
" <td> Fabio Luzzi</td>\n",
" <td> faio33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1405825932</td>\n",
" <td> 0</td>\n",
" <td> 9</td>\n",
" <td> 8060</td>\n",
" <td> 43295905@N05</td>\n",
" <td> Marvett Smith</td>\n",
" <td> Marvett Smith (Savoring the Simple)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1405535709</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 2888</td>\n",
" <td> 117312075@N06</td>\n",
" <td> Abhijit Roy</td>\n",
" <td> c4roy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1405488932</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 3794</td>\n",
" <td> 28465812@N07</td>\n",
" <td> </td>\n",
" <td> Monica & Cebolinha</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" contact family favedate friend iconfarm iconserver nsid \\\n",
"0 0 0 1406519587 0 4 3908 120825773@N05 \n",
"1 0 0 1406309214 0 3 2927 91007561@N05 \n",
"2 0 0 1406293554 0 4 3673 104486340@N03 \n",
"3 0 0 1406259611 0 3 2939 43738607@N07 \n",
"4 0 0 1406239666 0 4 3679 60142567@N05 \n",
"5 0 0 1406116570 0 5 4108 38030824@N04 \n",
"6 0 0 1405960894 0 3 2907 96274428@N05 \n",
"7 0 0 1405825932 0 9 8060 43295905@N05 \n",
"8 0 0 1405535709 0 3 2888 117312075@N06 \n",
"9 0 0 1405488932 0 4 3794 28465812@N07 \n",
"\n",
" realname username \n",
"0 Lukasan2014 \n",
"1 C\u01b0\u1eddng Euro | Photography \n",
"2 JM Fikko jm.fikko \n",
"3 .brianday \n",
"4 Paxtorino \n",
"5 christilou1 \n",
"6 Fabio Luzzi faio33 \n",
"7 Marvett Smith Marvett Smith (Savoring the Simple) \n",
"8 Abhijit Roy c4roy \n",
"9 Monica & Cebolinha "
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To summarize, what we have to do to access our data of interest and put it into a data frame is the following:\n",
"\n",
"- Use the api to retrieve the information in a json formatted string\n",
"- Remove some characters to make is a 'pure' json string readable by the python json package\n",
"- Then, here is the **trick:** convert the string to a dict structure with `json.load`. Why do that? Because the information about the faves is easier to extract by using `my_json_var['field1'][field2']` type of queries (extracting directly from the string would require regular expressions and we don't want that).\n",
"- Finally, pandas reads json as strings, so we convert it back with `json.dumps` and so can put it into a pandas data frame.\n",
"\n",
"_ **Edit:** I also tried it by using only data frames (without creating dict objects) but it required more manipulation._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we can view the dates as a time series"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_fav['favedate']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"0 1406519587\n",
"1 1406309214\n",
"2 1406293554\n",
"3 1406259611\n",
"4 1406239666\n",
"5 1406116570\n",
"6 1405960894\n",
"7 1405825932\n",
"8 1405535709\n",
"9 1405488932\n",
"Name: favedate, dtype: int64"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we want to convert the dates into a more readable format.\n",
"\n",
"For this we use the `to_datetime` function and specify that the UNIX timestamp unit is second (default is nanosecond)\n",
"\n",
"Additionally we may want to create a new data frame that only contains our date information."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2 = pd.DataFrame(pd.to_datetime(df_fav['favedate'], unit='s'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we are interested in the day of the week information, we have to create a `DateTimeIndex` object from which we can extract the `weekday` and create a new column of the data frame with such information"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2['weekday'] = pd.DatetimeIndex(df2['favedate']).weekday"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>favedate</th>\n",
" <th>weekday</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-07-28 03:53:07</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-07-25 17:26:54</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-07-25 13:05:54</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-07-25 03:40:11</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-07-24 22:07:46</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2014-07-23 11:56:10</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2014-07-21 16:41:34</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2014-07-20 03:12:12</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2014-07-16 18:35:09</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2014-07-16 05:35:32</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
" favedate weekday\n",
"0 2014-07-28 03:53:07 0\n",
"1 2014-07-25 17:26:54 4\n",
"2 2014-07-25 13:05:54 4\n",
"3 2014-07-25 03:40:11 4\n",
"4 2014-07-24 22:07:46 3\n",
"5 2014-07-23 11:56:10 2\n",
"6 2014-07-21 16:41:34 0\n",
"7 2014-07-20 03:12:12 6\n",
"8 2014-07-16 18:35:09 2\n",
"9 2014-07-16 05:35:32 2"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, so now we know those basic tools, we can start to visualize some stuff.\n",
"\n",
"Let's see if we can first visualize the number of favorites as a function of the time after posting a picture."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## II. Starting to explore the data\n",
"\n",
"In the following we define the number faves + comments as the activity.\n",
"\n",
"Things that may be interesting to look at: \n",
"\n",
"- Evolution of the activity with time\n",
" - For one photo\n",
" - For several photos (mean, variance)\n",
"- Relation between this time curve and the ranking of the picture in explore.\n",
"- Compare explore photos acativity evolution signatures with my average photos.\n",
"- Defining attack and decay could be interesting as an attibute for the time vs activity curve.\n",
"- Correlation between the week day the photo has been posted and the activity.\n",
"- Relation between hour of the post and total activity.\n",
"- Relation between the number of groups and activity.\n",
"\n",
"Here we chose the photo that has been ranked as first on the explore page and we try to see the evolution of the number of favorites with time.\n",
"\n",
"- First, we extract the first page of favorites, but we also need to get the number of pages which is in another field of the json dict object."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# picture that is 1st on explore page (date Jul 29th 2014)\n",
"pp = 50;\n",
"p_id = '14778520992' \n",
"str_tmp = flickr.photos_getFavorites(photo_id=p_id, per_page=pp);\n",
"json_tmp = json.loads(str_tmp[14:-1])\n",
"df_tmp = pd.read_json(json.dumps(json_tmp['photo']['person']));\n",
"df_fav = pd.DataFrame(pd.to_datetime(df_tmp['favedate'], unit='s'))\n",
"num_p = json_tmp['photo']['pages']\n",
"num_p"
],
"language": "python",
"metadata": {
"code_folding": []
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"20"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Because we work with relative time we need to extract the posting time and to convert it to a `pandas.Timestamp` object."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"str_tmp = flickr.photos_getInfo(photo_id=p_id);\n",
"tt = int(json.loads(str_tmp[14:-1])['photo']['dates']['posted'])\n",
"posted = pd.to_datetime(tt, unit='s')\n",
"posted"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"Timestamp('2014-07-29 18:44:12')"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Then we may continue extracting the rest of faves timestamps"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in arange(2, num_p+1):\n",
" str_tmp = \\\n",
" flickr.photos_getFavorites(photo_id=p_id, page=i, per_page=pp)\n",
"\n",
" json_tmp = json.loads(str_tmp[14:-1]);\n",
" df_tmp = pd.read_json(json.dumps(json_tmp['photo']['person']))\n",
" df_fav = df_fav.append(pd.DataFrame(pd.to_datetime(df_tmp['favedate'],\\\n",
" unit='s')), ignore_index=True)\n",
"#print \"data frame\\n\", df_fav"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Making a colum with time difference between post time and fave time"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_fav['diff'] = df_fav['favedate'] - posted"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Variable with time difference in hours"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ts=(df_fav['diff'].astype('timedelta64[s]').astype(float)/3600);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Finally we can draw a histogram of the evolution of number of favorites as this time difference. It gives us the dynamics of the number of favorites."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figsize(17,5)\n",
"ts.hist(bins=50, color='slategrey', histtype='stepfilled')\n",
"xlabel('hours'); ylabel('Number of favorites')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.text.Text at 0x7f1551003450>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7f15611b7850>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Let's see if we can prettify this by importing the `seaborn` package that is built on top of `matplotlib` for statistical visualization."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import seaborn as sb"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figsize(17,5)\n",
"ts.hist(bins=50, color='slategrey', histtype='stepfilled')\n",
"xlabel('hours'); ylabel('Number of favorites')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<matplotlib.text.Text at 0x7f15462ebd10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7f154633f950>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Nice! Now, we would like to visualize information not only for one photo, but for many others that also figure on the explore page. What we have to do is to extract the id's of these photos, repeat the fave extraction operation for each of them and stack the data in a dataframe. To do so im a more systematic way, we may want to define functions.\n",
"\n",
"##\u00a0III. Writing functions "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Extracts for a photo the times and types of events (fave or comment)\n",
"def getFlickrActivity(df_list):\n",
" pp = 50;\n",
" df = pd.DataFrame(columns=['time','event_type','photo_id'])\n",
" for p_id in df_list:\n",
" # parse first page (necessary to know the total number of pages)\n",
" str_tmp = flickr.photos_getFavorites(photo_id=p_id, per_page=pp)\n",
" json_tmp = json.loads(str_tmp[14:-1])\n",
" num_p = json_tmp['photo']['pages']\n",
" df_tmp = pd.read_json(json.dumps(json_tmp['photo']['person']))\n",
" dt = pd.Series(pd.to_datetime(df_tmp['favedate'], unit='s'))\n",
" df = df.append(pd.DataFrame({ 'time' : dt,\n",
" 'event_type' : 'f', \n",
" 'photo_id' : p_id}))\n",
" \n",
" # parse other pages\n",
" for i in arange(2, num_p+1):\n",
" str_tmp = \\\n",
" flickr.photos_getFavorites(photo_id=p_id, page=i, per_page=pp);\n",
" json_tmp = json.loads(str_tmp[14:-1])\n",
" df_tmp = pd.read_json(json.dumps(json_tmp['photo']['person']));\n",
" dt = pd.Series(pd.to_datetime(df_tmp['favedate'], unit='s'))\n",
" df = df.append(pd.DataFrame({ 'time' : dt,\n",
" 'event_type' : 'f', \n",
" 'photo_id' : p_id}), ignore_index=True)\n",
" \n",
" # do the same with comments (no page parsing required)\n",
" str_tmp = json.loads(flickr.photos_comments_getList(photo_id=p_id)[14:-1])\n",
" df_tmp = pd.read_json(json.dumps(str_tmp['comments']['comment']))\n",
" dt = pd.Series(pd.to_datetime(df_tmp['datecreate'], unit='s'))\n",
" df = df.append(pd.DataFrame({ 'time' : dt,\n",
" 'event_type' : 'c', \n",
" 'photo_id' : p_id}), ignore_index=True)\n",
"\n",
" return df"
],
"language": "python",
"metadata": {
"code_folding": []
},
"outputs": [],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Takes a data frame containing a list of photos and extracts infomation for each of them\n",
"# time posted, views (to complete in future versions)\n",
"def getFlickrInfo(df_list):\n",
" pp = 50;\n",
" df_out = pd.DataFrame(index = arange(df_list.size), columns = ['photo_id', 'posted', 'views'])\n",
" for idx, p_id in enumerate(df_list):\n",
" \n",
" str_tmp = flickr.photos_getInfo(photo_id=p_id)\n",
" tt = int(json.loads(str_tmp[14:-1])['photo']['dates']['posted'])\n",
" posted = pd.to_datetime(tt, unit='s')\n",
" views = int(json.loads(str_tmp[14:-1])['photo']['views'])\n",
" \n",
" df_out.posted.ix[idx] = posted;\n",
" df_out.views.ix[idx] = views;\n",
" df_out.photo_id.ix[idx] = p_id;\n",
"\n",
" return df_out"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 111
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Let's try to see if we could use this with the photos published in explore ('interesting' ones according to flickr) by defining a function that retrieves the list of these:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def getFlickrExploreList():\n",
" str_tmp = flickr.interestingness_getList()\n",
" json_tmp = json.loads(str_tmp[14:-1])\n",
" df_tmp = pd.read_json(json.dumps(json_tmp['photos']['photo']))\n",
" df = df_tmp['id']\n",
" return df # list of photo ids"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 112
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Making a data frame with pictures from explore\n",
"df_list = getFlickrExploreList()\n",
"df_info = getFlickrInfo(df_list)\n",
"df_event = getFlickrActivity(df_list)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 158
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have a data frame `df_info` that contains for each picture the time posted and the number of views (which we couls also count as a part of the activity),"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_info.tail()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>photo_id</th>\n",
" <th>posted</th>\n",
" <th>views</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>95</th>\n",
" <td> 14944614512</td>\n",
" <td>2014-08-17 11:04:56</td>\n",
" <td> 10970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td> 14924191006</td>\n",
" <td>2014-08-17 15:15:35</td>\n",
" <td> 9145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td> 14764195088</td>\n",
" <td>2014-08-17 20:56:48</td>\n",
" <td> 10851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td> 14758390478</td>\n",
" <td>2014-08-17 11:10:59</td>\n",
" <td> 12199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td> 14955273955</td>\n",
" <td>2014-08-18 06:02:39</td>\n",
" <td> 1665</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 159,
"text": [
" photo_id posted views\n",
"95 14944614512 2014-08-17 11:04:56 10970\n",
"96 14924191006 2014-08-17 15:15:35 9145\n",
"97 14764195088 2014-08-17 20:56:48 10851\n",
"98 14758390478 2014-08-17 11:10:59 12199\n",
"99 14955273955 2014-08-18 06:02:39 1665"
]
}
],
"prompt_number": 159
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and another one `df_event` that contains the list of events (fave and comments) for our picture list"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_event.tail()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>event_type</th>\n",
" <th>photo_id</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>35718</th>\n",
" <td> c</td>\n",
" <td> 14955273955</td>\n",
" <td>2014-08-18 15:35:11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35719</th>\n",
" <td> c</td>\n",
" <td> 14955273955</td>\n",
" <td>2014-08-18 15:43:48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35720</th>\n",
" <td> c</td>\n",
" <td> 14955273955</td>\n",
" <td>2014-08-18 18:02:35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35721</th>\n",
" <td> c</td>\n",
" <td> 14955273955</td>\n",
" <td>2014-08-18 18:56:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35722</th>\n",
" <td> c</td>\n",
" <td> 14955273955</td>\n",
" <td>2014-08-18 19:50:20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 160,
"text": [
" event_type photo_id time\n",
"35718 c 14955273955 2014-08-18 15:35:11\n",
"35719 c 14955273955 2014-08-18 15:43:48\n",
"35720 c 14955273955 2014-08-18 18:02:35\n",
"35721 c 14955273955 2014-08-18 18:56:43\n",
"35722 c 14955273955 2014-08-18 19:50:20"
]
}
],
"prompt_number": 160
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figsize(8,6)\n",
"tt=pd.DatetimeIndex(df_event['time']);\n",
"hist(tt.hour, bins=24, histtype='stepfilled', alpha=0.7);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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SCmK4JUkqiOGWJKkghluSpIIYbkmSCmK4JUkqiOGWJKkghluSpIIYbkmSCmK4JUkqiOGW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\n",
"text": [
"<matplotlib.figure.Figure at 0x10b026050>"
]
}
],
"prompt_number": 161
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figsize(8,6)\n",
"tt=pd.DatetimeIndex(df_info['posted']);\n",
"hist(tt.hour, bins=24, histtype='stepfilled', alpha=0.7);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10ccd1110>"
]
}
],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"grouped = df_event.groupby('photo_id')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 163
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figsize(15,7)\n",
"b=arange(0, 24)\n",
"acc = zeros(len(b)-1)\n",
"for k, grp in grouped:\n",
" tt=pd.DatetimeIndex(grp['time'])\n",
" h,hh=histogram(tt.hour, bins=b)\n",
" acc += h\n",
" plot(b[:-1],h, '.r', alpha=0.25, markersize=10)\n",
"acc /= len(grouped)\n",
"plot(b[:-1],acc)\n",
"gca().invert_yaxis()\n",
"gca().xaxis.tick_top()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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QMw0y2k4S2u7d2KKFkJw2YgpwJxLndHzbrxupuMHH7lxBWybGj9btYu0ze09r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\n0EjlmuuqXSHj0zRxa4q2TpV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37kEZ+f/t3X1wXNd53/EfgH3BXeKNpABS4Zto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"text": [
"<matplotlib.figure.Figure at 0x10cd03cd0>"
]
}
],
"prompt_number": 173
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figsize(15,7)\n",
"nbin = 100\n",
"b=arange(0, 48)\n",
"acc = zeros(len(b)-1)\n",
"for k, grp in grouped:\n",
" tt=grp['time']-df_info.posted[df_info.photo_id==k].iloc[0]\n",
" #print tt\n",
" ttt=(tt.astype('timedelta64[s]').astype(float)/3600);\n",
" h,hh=histogram(ttt, bins=b)\n",
" acc += h\n",
" plot(b[:-1], h, '.r', alpha=0.25, markersize=10)\n",
"acc /= len(grouped)\n",
"plot(b[:-1], acc)\n",
"gca().invert_yaxis()\n",
"gca().xaxis.tick_top()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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pd/eKL0EF7GTStyxYRtXVz4+DZ5BBMgFl4WEszM/7E5aXxVQWR9P8SZAllvnZ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\n64+cUaO1iMuXc+jll5IkVx94cODBzE/tHgAMS/AG3JjOguUcP56r3cP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"text": [
"<matplotlib.figure.Figure at 0x10cda0ad0>"
]
}
],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}