I am having a hard time summing two dates that are saved in two separate json files. I want to add set dates together which are saved in separate libraries.
The first file (A1.json) contains: {"expires": "2019-09-11"}
The second file (Whitelist.json) contains: {"expires": "0000-01-00"}
These dates are created by using tkcalendar and are later exported to these seperate files, the idea being that summing them lets me set a time date one month into the future. However, I can't seem to add them together without some form of an error.
I have tried converting the json files to strings in python and then adding them and also using the striptime command to sum the dates.
Here is the relevant chunk of the code:
{with open('A1.json') as f:
data=json.loads(f.read())
for material in data.items():
A1 = (format(material[1]['expires']))
with open('Whitelist.json') as f:
data=json.loads(f.read())
for material in data.items():
A2 = (format(material[1]['expires']))
print(A1+A2)}
When this is used, they just get pasted one after another. They don't get summed the way I need.
I also have tried the following code:
{t1 = dt.datetime.strptime('A1', '%d-%m-%Y')
t2 = dt.datetime.strptime('Whitelist', '%d-%m-%Y')
time_zero = dt.datetime.strptime('00:00:00', '%d/%m/%Y')
print((t1 - time_zero + Whitelist).time())}
However, this constantly gives out ValueError: time data does not match format '%y:%m:%d'.
What I expect is the sum of 2019-09-11 and 0000-01-00's result is 2019-10-11. However, the result is 2019-09-110000-01-00. Trying the strptime method gives out ValueErrors such as: ValueError: time data does not match format '%y:%m:%d'.
Thank you in advance, and I apologize if I did something wrong on my first post.
Use pandas:
the actual format of the json file isn't provided, so use something like the following to get the data into a DataFrame:
pd.read_json('A1.json', orient='records'): parameters will depend on the format of the file
json_normalize
d2 is not a proper datetime format so don't try to convert it.
the Code section below, will use a dict to set up the DataFrame for the example.
json files to DataFrames:
df1 = pd.read_json('A1.json', orient='records')
df2 = pd.read_json('Whitelist.json', orient='records')
df = pd.DataFrame()
df['expires'] = df1.expires
df['d2'] = df2.expires
Code:
import pandas as pd
df = pd.DataFrame({"expires": ["2019-09-11", "2019-10-11", "2019-11-11"],
"d2": ["0000-01-00", "0000-02-00", "0000-03-00"]})
Expand d2 using str.split:
df.expires = pd.to_datetime(df.expires)
df[['y', 'm', 'd']] = df.d2.str.split('-', expand=True)
Use pd.DateOffset:
df['expires_new'] = df[['expires', 'm']].apply(lambda x: x[0] + pd.DateOffset(months=int(x[1])), axis=1)
if d2 is expected to have more than just a new m or month value, the lambda expression can be changed to call a function that adjusts for y, m, and d values.
Related
My data come from BigQuery exported to GCS bucket as CSV file and if the file size is quite massive, BigQuery will automatically split the data into several chunk. With time series in mind, the time series might be scattered across different files. I have a custom function that I want to applied to each TimeseriesID.
Here's some constraint of the data:
The data is sorted by TimeseriesID and TimeID
The number of row of each files is may vary, but at minimum 1 row (which is very unlikely)
The starting of TimeID is not always 0
The length of each time series may vary but at maximum it will only scattered across 2 files. No time series scatter in 3 different files.
Here's the initial setup to illustrate the problem:
# Please take note this is just for simplicity. The actual goal is not to calculate mean for all group, but to apply a custom_func to each Timeseries ID
def custom_func(x):
return np.mean(x)
# Please take note this is just for simplicity. In actual, I read the file one by one since reading all the data is not possible
df1 = pd.DataFrame({"TimeseriesID":['A','A','A','B'],"TimeID":[0,1,2,4],"value":[10,20,5,30]})
df2 = pd.DataFrame({"TimeseriesID":['B','B','B','C'],"TimeID":[5,6,7,8],"value":[10,20,5,30]})
df3 = pd.DataFrame({"TimeseriesID":['C','D','D','D'],"TimeID":[9,1,2,3],"value":[10,20,5,30]})
This should be pretty trivial if I can just concat all the files but the problem is if I concat all the dataframe then it won't fit in the memory.
The output I desired is should be similar to this but without concat all the files.
pd.concat([df1,df2,df3],axis=0).groupby('TimeseriesID').agg({"value":simple_func})
I'm also aware about vaex and dask but I want to stick with simple pandas for time being.
I'm also open to solution which involve modifying the BigQuery to split the files better.
Approach presented by op to use concat with million of records would be overkill for memories/other resources.
I have tested OP code using Google Colab Nootebooks and this was a bad approach
import pandas as pd
import numpy as np
import time
# Please take note this is just for simplicity. The actual goal is not to calculate mean for all group, but to apply a custom_func to each Timeseries ID
def custom_func(x):
return np.mean(x)
# Please take note this is just for simplicity. In actual, I read the file one by one since reading all the data is not possible
df1 = pd.DataFrame({"TimeseriesID":['A','A','A','B'],"TimeID":[0,1,2,4],"value":[10,20,5,30]})
df2 = pd.DataFrame({"TimeseriesID":['B','B','B','C'],"TimeID":[5,6,7,8],"value":[10,20,5,30]})
df3 = pd.DataFrame({"TimeseriesID":['C','D','D','D'],"TimeID":[9,1,2,3],"value":[10,20,5,30]})
start = time.time()
df = pd.concat([df1,df2,df3]).groupby('TimeseriesID').agg({"value":custom_func})
elapsed = (time.time() - start)
print(elapsed)
print(df.head())
output will be:
0.023952960968017578
value
TimeseriesID A 11.666667
B 16.250000
C 20.000000
D 18.333333
As you can see, 'concat' takes time to process. Due to few records this is not perceived.
The approach should be as follow:
Get files with data that you are going to process. ie: only workable columns.
Create a dictionary from the processed files key and values. if necessary, obtain values per key in a necessary file. You can store the results in a 'results' directory as json/csv:
A.csv will have all key 'A' values
...
n.csv will have all key 'n' values
Iterate trough results directory and start building your final output inside a dictionary.
{'A': [10, 20, 5], 'B': [30, 10, 20, 5], 'C': [30, 10], 'D': [20, 5, 30]}
apply custom function to each key value list.
{'A': 11.666666666666666, 'B': 16.25, 'C': 20.0, 'D': 18.333333333333332}
You can check the logic using below code, I use json to store the data:
from google.colab import files
import json
import pandas as pd
#initial dataset
df1 = pd.DataFrame({"TimeseriesID":['A','A','A','B'],"TimeID":[0,1,2,4],"value":[10,20,5,30]})
df2 = pd.DataFrame({"TimeseriesID":['B','B','B','C'],"TimeID":[5,6,7,8],"value":[10,20,5,30]})
df3 = pd.DataFrame({"TimeseriesID":['C','D','D','D'],"TimeID":[9,1,2,3],"value":[10,20,5,30]})
#get unique keys and its values
df1.groupby('TimeseriesID')['value'].apply(list).to_json('df1.json')
df2.groupby('TimeseriesID')['value'].apply(list).to_json('df2.json')
df3.groupby('TimeseriesID')['value'].apply(list).to_json('df3.json')
#as this is an example you can download the output as jsons
files.download('df1.json')
files.download('df2.json')
files.download('df3.json')
Update 06/10/2021
I have tuned code for OPs needs. This part creates refined files.
from google.colab import files
import json
#you should use your own function to get the data from the file
def retrieve_data(uploaded,file):
return json.loads(uploaded[file].decode('utf-8'))
#you should use your own function to get a list of files to process
def retrieve_files():
return files.upload()
key_list =[]
#call a function that gets a list of files to process
file_to_process = retrieve_files()
#read every raw file:
for file in file_to_process:
file_data = retrieve_data(file_to_process,file)
for key,value in file_data.items():
if key not in key_list:
key_list.append(key)
with open(f'{key}.json','w') as new_key_file:
new_json = json.dumps({key:value})
new_key_file.write(new_json)
else:
with open(f'{key}.json','r+') as key_file:
raw_json = key_file.read()
old_json = json.loads(raw_json)
new_json = json.dumps({key:old_json[key]+value})
key_file.seek(0)
key_file.write(new_json)
for key in key_list:
files.download(f'{key}.json')
print(key_list)
Update 07/10/2021
I have updated code to avoid confusion. This part process refined files.
import time
import numpy as np
#Once we get the refined values we can use it to apply custom functions
def custom_func(x):
return np.mean(x)
#Get key and data content from single json
def get_data(file_data):
content = file_data.popitem()
return content[0],content[1]
#load key list and build our refined dictionary
refined_values = []
#call a function that gets a list of files to process
file_to_process = retrieve_files()
start = time.time()
#read every refined file:
for file in file_to_process:
#read content of file n
file_data = retrieve_data(file_to_process,file)
#parse and apply function per file read
key,data = get_data(file_data)
func_output = custom_func(data)
#start building refined list
refined_values.append([key,func_output])
elapsed = (time.time() - start)
print(elapsed)
df = pd.DataFrame.from_records(refined_values,columns=['TimerSeriesID','value']).sort_values(by=['TimerSeriesID'])
df = df.reset_index(drop=True)
print(df.head())
output will be:
0.00045609474182128906
TimerSeriesID value
0 A 11.666667
1 B 16.250000
2 C 20.000000
3 D 18.333333
summarize:
When handling large datasets, you should always need to focus on the data that you are going to use and keep it minimal. Only using the workable values.
Processing times are faster when operations are performed by basic operators or python native libraries.
I have a dataframe with two columns containing dates non formated.
the data in such columns is as follows:
2011-06-10T00:00:00.000+02:00
I would like to get just the date and format it.
In a Jupyter notebook I do the followings:
sections['produced'] = pd.to_datetime(sections['produced'])
sections['produced'] = [d.strftime('%Y-%m-%d') if not pd.isnull(d) else '' for d in sections['produced']]
sections['updated'] = pd.to_datetime(sections['updated'])
sections['updated'] = [d.strftime('%Y-%m-%d') if not pd.isnull(d) else '' for d in sections['updated']]
sections.info()
Then I print out the sections dataframe and indeed the dates are printed correctly.
BUT:
sections.info()
still tells me that those columns are non-null objects and not datetime.
Why?
secondly, my approach does not seem to work under the hood, i.e. the date types are not actually dates.
What should I do?
And last, the code is super verbose for something that should be one liner, or not? (i.e. pandas is powerful but has his limits)
EDIT 1: Answering some of the contributors. I expect datetime. just 2008-02-02 just the day.
So when doing:
sections['updated'] = pd.to_datetime(sections['updated'])
the date type is converted.
but when doing next:
sections['produced'] = [d.strftime('%Y-%m-%d') if not pd.isnull(d) else '' for d in sections['produced']]
So the aim here is to a) covert to datetime format b) get the date format 2008-01-02, I dont care about seconds c) it has to be printed out in jupyter notebook as such, i.e. as date
just pass errors parameter in to_datetime() method and set that equal to 'coerce':-
sections['produced'] = pd.to_datetime(sections['produced'],errors='coerce')
sections['updated'] = pd.to_datetime(sections['updated'],errors='coerce')
This should work as a one liner:
df[['produced','updated']] = df[['produced','updated']].apply(lambda x: pd.to_datetime(x,errors='coerce'))
I wanted to try uploading a series of items to test.wikidata, creating the item and then adding a statement of inception P571. The csv file sometimes has a date value, sometimes not. When no date value is given, I want to write out a placeholder 'some value'.
Imagine a dataframe like this:
df = {'Object': [1, 2,3], 'Date': [250,,300]}
However, I am not sure using Pywikibot how to iterate over a csv file with pywikibot to create an item for each row and add a statement. Here is the code I wrote:
import pywikibot
import pandas as pd
site = pywikibot.Site("test", "wikidata")
repo = site.data_repository()
df = pd.read_csv('experiment.csv')
item = pywikibot.ItemPage(repo)
for item in df:
date = df['date']
prop_date = pywikibot.Claim(repo, u'P571')
if date=='':
prop_date.setSnakType('somevalue')
else:
target = pywikibot.WbTime(year=date)
prop_date.setTarget(target)
item.addClaim(prop_date)
When I run this through PAWS, I get the message: KeyError: 'date'
But I think the real issue here is that I am not sure how to get Pywikibot to iterate over each row of the dataframe and create a new claim for each new date value. I would value any feedback or suggestions for good examples and documentation. Many thanks!
Looking back on this, the solution was to use .iterrows() or .itertuples() or .loc[] to access the values in the row.
So
for item in df.itertuples():
prop_date = pywikibot.Claim(repo, u'P571')
if item.date=='':
prop_date.setSnakType('somevalue')
else:
target = pywikibot.WbTime(year=date)
prop_date.setTarget(target)
item.addClaim(prop_date)
I am fairly new to python and coding in general.
I have a big data file that provides daily data for the period 2011-2018 for a number of stock tickers (300~).
The data is a .csv file with circa 150k rows and looks as follows (short example):
Date,Symbol,ShortExemptVolume,ShortVolume,TotalVolume
20110103,AAWW,0.0,28369,78113.0
20110103,AMD,0.0,3183556,8095093.0
20110103,AMRS,0.0,14196,18811.0
20110103,ARAY,0.0,31685,77976.0
20110103,ARCC,0.0,177208,423768.0
20110103,ASCMA,0.0,3930,26527.0
20110103,ATI,0.0,193772,301287.0
20110103,ATSG,0.0,23659,72965.0
20110103,AVID,0.0,7211,18896.0
20110103,BMRN,0.0,21740,213974.0
20110103,CAMP,0.0,2000,11401.0
20110103,CIEN,0.0,625165,1309490.0
20110103,COWN,0.0,3195,24293.0
20110103,CSV,0.0,6133,25394.0
I have a function that allows me to filter for a specific symbol and get 10 observations before and after a specified date (could be any date between 2011 and 2018).
import pandas as pd
from datetime import datetime
import urllib
import datetime
def get_data(issue_date, stock_ticker):
df = pd.read_csv (r'D:\Project\Data\Short_Interest\exampledata.csv')
df['Date'] = pd.to_datetime(df['Date'], format="%Y%m%d")
d = df
df = pd.DataFrame(d)
short = df.loc[df.Symbol.eq(stock_ticker)]
# get the index of the row of interest
ix = short[short.Date.eq(issue_date)].index[0]
# get the item row for that row's index
iloc_ix = short.index.get_loc(ix)
# get the +/-1 iloc rows (+2 because that is how slices work), basically +1 and -1 trading days
short_data = short.iloc[iloc_ix-10: iloc_ix+11]
return [short_data]
I want to create a script that iterates a list of 'issue_dates' and 'stock_tickers'. The list (a .csv) looks as following:
ARAY,07/08/2017
ARAY,24/04/2014
ACETQ,16/11/2015
ACETQ,16/11/2015
NVLNA,15/08/2014
ATSG,29/09/2017
ATI,24/05/2016
MDRX,18/06/2013
MDRX,18/06/2013
AMAGX,10/05/2017
AMAGX,14/02/2014
AMD,14/09/2016
To break down my problem and question I would like to know how to do the following:
First, how do I load the inputs?
Second, how do I call the function on each input?
And last, how do I accumulate all the function returns in one dataframe?
To load the inputs and call the function for each row; iterate over the csv file and pass each row's values to the function and accumulate the resulting Seriesin a list.
I modified your function a bit: removed the DataFrame creation so it is only done once and added a try/except block to account for missing dates or tickers (your example data didn't match up too well). The dates in the second csv look like they are day/month/year so I converted them for that format.
import pandas as pd
import datetime, csv
def get_data(df, issue_date, stock_ticker):
'''Return a Series for the ticker centered on the issue date.
'''
short = df.loc[df.Symbol.eq(stock_ticker)]
# get the index of the row of interest
try:
ix = short[short.Date.eq(issue_date)].index[0]
# get the item row for that row's index
iloc_ix = short.index.get_loc(ix)
# get the +/-1 iloc rows (+2 because that is how slices work), basically +1 and -1 trading days
short_data = short.iloc[iloc_ix-10: iloc_ix+11]
except IndexError:
msg = f'no data for {stock_ticker} on {issue_date}'
#log.info(msg)
print(msg)
short_data = None
return short_data
df = pd.read_csv (datafile)
df['Date'] = pd.to_datetime(df['Date'], format="%Y%m%d")
results = []
with open('issues.csv') as issues:
for ticker,date in csv.reader(issues):
day,month,year = map(int,date.split('/'))
# dt = datetime.datetime.strptime(date, r'%d/%m/%Y')
date = datetime.date(year,month,day)
s = get_data(df,date,ticker)
results.append(s)
# print(s)
Creating a single DataFrame or table for all that info may be problematic especially since the date ranges are all different. Probably should ask a separate question regarding that. Its mcve should probably just include a few minimal Pandas Series with a couple of different date ranges and tickers.
I have a use case where I need to create a python dictionary with year and months and then concatenate all the dataframes to single dataframe. I have done the implementation as below:
dict_year_month = {}
temp_dict_1={}
temp_dict_2={}
for ym in [201104,201105 ... 201706]:
key_name = 'df_'+str(ym)+'A'
temp_dict_1[key_name]=df[(df['col1']<=ym) & (df['col2']>ym)
& (df['col3']==1)]
temp_dict_2[key_name]=df[(df['col1']<=ym) & (df['col2']==0)
& (df['col3']==1)]
if not temp_dict_1[key_name].empty:
dict_year_month [key_name] =temp_dict_1[key_name]
dict_year_month [key_name].loc[:, 'new_col'] = ym
elif not temp_dict_2[key_name].empty:
dict_year_month [key_name] =temp_dict_2[key_name]
dict_year_month [key_name].loc[:, 'new_col'] = ym
dict_year_month [key_name]=dict_year_month [key_name].sort_values('col4')
dict_year_month [key_name]=dict_year_month [key_name].drop_duplicates('col5')
.. do some other processing
create individual dataframes as df_201104A .. and so on ..
dict_year_month
#concatenate all the above individual dataframe into single dataframe:
df1 = pd.concat([
dict_year_month['df_201104A'],dict_year_month['df_201105A'],
... so on till dict_year_month['df_201706A'])
Now the challenge is I have to rerun the set of code on each quarter so every time I have to update this script with new yearmonths dict key and in pd.concat as well needs to updated with new year month details. I am looking for some other solution by which I can probably read the keys and create a list of dataframes in concatenate from a properties file or config file?
There are only a few things you need to do to get there - the first is just to enumerate through the months between your start and end month, which I do below using rrule, reading in the start and end dates from a file. This gets you the keys for your dictionary. Then just use the .values() method on the dictionaries to get all the dataframes.
from dateutil import rrule
from datetime import datetime, timedelta
import pickle
#get these from whereever, config, etc.
params = {
'start_year':2011,
'start_month':4,
'end_year':2017,
'end_month':6,
}
pickle.dump(params, open("params.pkl", "wb"))
params = pickle.load(open("params.pkl", "rb"))
start = datetime(year=params['start_year'], month=params['start_month'], day=1)
end = datetime(year=params['end_year'], month=params['end_month'], day=1)
keys = [int(dt.strftime("%Y%m")) for dt in rrule.rrule(rrule.MONTHLY, dtstart=start, until=end)]
print(keys)
## Do some things and get a dict
dict_year_month = {'201104':pd.DataFrame([[1, 2, 3]]), '201105':pd.DataFrame([[4, 5, 6]])} #... etc
pd.concat(dict_year_month.values())
The pickle file is to show one way of saving and loading parameters - it is a binary format so manually editing the parameters wouldn't really work. You might want to investigate something like yaml to get more sophisticated, feel free to ask a new question if you need help with that.