def single_ticker(ticker):
form_data = {
"request":
json.dumps({
"tickers": [],
"filters": [{
"column": "dataset__entity__entity_ticker__ticker__ticker",
"type": "=",
"value": [ticker]
}],
}),
'start':
1,
'limit':
1000,
}
items_df = thinknum_pull._get_data_multi_loop(dataset_name, form_data)
#items_df["YMD"] = items_df["As Of Date"].apply(lambda x: x.encode('ascii','ignore')[:8])
#items_df["YMD"] = pd.to_datetime(items_df["YMD"])
#items_df['MONTH'] = items_df.YMD.dt.strftime("%Y-%m")
items_df['As Of Date'] = pd.to_datetime(items_df['As Of Date'],
format="%Y-%m-%d %H:%M:%S")
#items_df['MONTH'] = pd.to_datetime(items_df['MONTH'])
#items_df = items_df.sort_values(by=['YMD'], ascending = True, kind = 'mergesort')
return items_df
def all_tickers():
form_data = {
"request": json.dumps({
"tickers": ['nyse:mcd'],
"groups": [
{
"column": "dataset__entity__entity_ticker__ticker__ticker"
}
],
"aggregations": [
{
"column": "date_added",
"type": "min"
},
{
"column": "date_added",
"type": "max"
},
{
"column": "date_updated",
"type": "min"
},
{
"column": "date_updated",
"type": "max"
}]
}),
'start': 1,
'limit': 1000,
}
items_df = thinknum_pull._get_data_multi_loop(dataset_name, form_data)
#items_df['As Of Date'] = pd.to_datetime(items_df['As Of Date'],format = "%Y-%m-%d %H:%M:%S")
return items_df
def all_ticker():
form_data = {
"request": json.dumps({
"tickers": [],
}),
'start': 1,
'limit': 1000,
}
items_df = thinknum_pull._get_data_multi_loop(dataset_name, form_data)
items_df['As Of Date'] = pd.to_datetime(items_df['As Of Date'],format = "%Y-%m-%d %H:%M:%S")
return items_df
def single_ticker(ticker):
form_data = {
"request": json.dumps({
"tickers": [],
"filters": [
{
"column": "dataset__entity__entity_ticker__ticker__ticker",
"type": "=",
"value": [ticker]
}],
}),
'start': 1,
'limit': 1000,
}
items_df = thinknum_pull._get_data_multi_loop(dataset_name, form_data)
items_df['As Of Date'] = pd.to_datetime(items_df['As Of Date'],format = "%Y-%m-%d %H:%M:%S")
return items_df
import seaborn as sns
#import s3_pull
import glob
from os.path import normpath, basename
import datetime
dataset_name = 'social_facebook_app'
form_data = {
'request': json.dumps({
'tickers': [],
}),
'start': 1,
'limit': 1000,
}
data = thinknum_pull._get_data_multi_loop(dataset_name, form_data)
data = data.drop_duplicates()
#data.at[820,'Category'] = 'Lifestyle'
#cat_data = data.sort_values(['Category','Daily Active Users'], ascending=[False,False])
#col = ['Name','Daily Active Users','Weekly Active Users','Monthly Active Users','Category']
#cat_data = cat_data[col]
#index = cat_data['Category']==''
#cat_data.at[index,'Category'] = 'Not Specified'
##top 5 apps in each category by daily active users
#util_top = cat_data[cat_data['Category']=='Utilities'].head(9)
#life_top = cat_data[cat_data['Category']=='Lifestyle'].head(10)
#games_top = cat_data[cat_data['Category']=='Games'].head(6)
#ent_top = cat_data[cat_data['Category']=='Entertainment'].head(17)
#bus_top = cat_data[cat_data['Category']=='Business'].head(5)
#sport_top = cat_data[cat_data['Category']=='Sports'].head(5)
#na_top = cat_data[cat_data['Category']=='Not Specified'].head(5)