ebt | Flexible backup framework | Continuous Backup library
kandi X-RAY | ebt Summary
kandi X-RAY | ebt Summary
This is backup framework for creating flexible backup scripts.
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Top functions reviewed by kandi - BETA
- Return the command line interface
- Add file handler
- Add a syslog handler
- Add a SMTP handler
- Create a new instance
- Creates a VM snapshot
- Create a snapshot XML file
- Removes a VM snapshot
- Start the VM
- Returns a filtered list of domains
- Creates a new instance backup
- Create a new instance backup
- Export domain to XML
- Returns a list of the disks of a domain
- Start the stream
- Write data to file
- Start the data generator
- Diff two files
- Start the vault
- Download a file from the vault
- Start the backup
- Start the virtual machine
- Backup an instance
- Emit a record
- Copy files from source to destination
- Create a new instance and save it
ebt Key Features
ebt Examples and Code Snippets
Lines of Code : 29License : Strong Copyleft (CC BY-SA 4.0)
Copys = '\$(000)'
years = range(2018, 2021)
df.assign(**{
f'SBL {y} {s}': df.filter(regex=fr'Small Business Loans.*{y}.*{s}').sum(1)
for y in years
})
df.assign(**{
f'Loans {y} {s}': df.filter(regex=fr'(Mucols = ['1Q16','2Q16','3Q16']
df[cols].gt(0).sum()
1Q16 5
2Q16 5
3Q16 4
dtype: int64
df = pd.merge_asof(daily, sf1.drop(columns=['dimension', 'calendardate',
'reportperiod','lastupdated',
'ev', 'evebit', 'evebitda',
daily['date']=pd.to_datetime(daily['date'])
sf1['calendardate']=pd.to_datetime(sf1['calendardate'])
df = pd.merge_asof(daily, sf1, by = 'ticker', left_on='date', right_on='calendardate', tolerance=pd.Timedelta(valudaily = daily.sort_values(['date'])
sf1 = sf1.sort_values(['calendardate'])
def set_vals(row):
result = ''
if row['ticker'] == 'AAPL':
result = 'something1'
elif row['ticker'] == 'GOOGL':
result = 'something2'
return result
df['sector'] = df.apply(set_vals,axis=1)
df
df.lolist = ['GOOG', 'AAPL', 'AMZN', 'NFLX']
first = True
for tickers in list:
df1 = df[df.ticker == tickers]
if first:
df1.to_csv("20CompanyAnalysisData1.csv", mode='a', header=True)
first = False
else:
df EBT carry_forward
year
2021 -377893.353711 0
2022 -282754.978037 0
2023 -224512.990469 0
2024 -167696.637680 0
df["new = []
for row in rsfMRI_timeseries_2d:
new.append(np.take(row, find_bootstrap_indices).tolist())
new = rsfMRI_timeseries_2d[:,find_bootstrap_indices].tolist()
new = rsfMRI_timesedf.reset_index(inplace=True)
df['hour'] = df['index'].apply(lambda x: x[:-2])
df['minute'] = df['index'].apply(lambda x: x{-2:]
hourly = df.groupby(by='hour').sum()
Community Discussions
Trending Discussions on ebt
QUESTION
I have a dataframe:
...ANSWER
Answered 2021-Jun-08 at 22:19Are the columns are you summing always the same? That is, are there always 3 2019 columns with those same names, and 3 2020 columns with those names? If so, you can just hardcode those new columns.
QUESTION
I have a dataframe
...ANSWER
Answered 2021-May-05 at 00:19So you need to do
QUESTION
I have a dataframe:
...ANSWER
Answered 2021-May-03 at 17:56With your shown samples, please try following.
QUESTION
I have a data frame:
...ANSWER
Answered 2021-May-03 at 17:23lst = df.filter(regex=r"\dQ\d+").gt(0).sum().tolist()
print(lst)
QUESTION
I have a dataframe:
...ANSWER
Answered 2021-Mar-19 at 21:14Maybe you're looking for .diff() function?
QUESTION
I have a dataframe:
...ANSWER
Answered 2021-Mar-13 at 22:09You can collect your divisor rules into a dictionary:
QUESTION
Long question: I have two CSV files, one called SF1 which has quarterly data (only 4 times a year) with a datekey column, and one called DAILY which gives data every day. This is financial data so there are ticker columns.
I need to grab the quarterly data for SF1 and write it to the DAILY csv file for all the days that are in between when we get the next quarterly data.
For example, AAPL has quarterly data released in SF1 on 2010-01-01 and its next earnings report is going to be on 2010-03-04. I then need every row in the DAILY file with ticker AAPL between the dates 2010-01-01 until 2010-03-04 to have the same information as that one row on that date in the SF1 file.
So far, I have made a python dictionary that goes through the SF1 file and adds the dates to a list which is the value of the ticker keys in the dictionary. I thought about potentially getting rid of the previous string and just referencing the string that is in the dictionary to go and search for the data to write to the DAILY file.
Some of the columns needed to transfer from the SF1 file to the DAILY file are:
['accoci', 'assets', 'assetsavg', 'assetsc', 'assetsnc', 'assetturnover', 'bvps', 'capex', 'cashneq', 'cashnequsd', 'cor', 'consolinc', 'currentratio', 'de', 'debt', 'debtc', 'debtnc', 'debtusd', 'deferredrev', 'depamor', 'deposits', 'divyield', 'dps', 'ebit']
Code so far:
...ANSWER
Answered 2021-Feb-27 at 12:10The solution is merge_asof it allows to merge date columns to the closer immediately after or before in the second dataframe.
As is it not explicit, I will assume here that daily.date and sf1.datekey are both true date columns, meaning that their dtype is datetime64[ns]. merge_asof cannot use string columns with an object dtype.
I will also assume that you do not want the ev evebit evebitda marketcap pb pe and ps columns from the sf1 dataframes because their names conflict with columns from daily (more on that later):
Code could be:
QUESTION
I'm trying to merge to pandas dataframes, one is called DAILY and the other SF1.
DAILY csv:
...ANSWER
Answered 2021-Feb-27 at 16:26You are facing this problem because your date column in 'daily' and calendardate column in 'sf1' are of type object i.e string
Just change their type to datatime by pd.to_datetime() method
so just add these 2 lines of code in your Datasorting/cleaning code:-
QUESTION
I'm trying to merge two Pandas dataframes, one called SF1 with quarterly data, and one called DAILY with daily data.
Daily dataframe:
...ANSWER
Answered 2021-Feb-27 at 19:10The sorting by ticker is not necessary as this is used for the exact join. Moreover, having it as first column in your sort_values calls prevents the correct sorting on the columns for the backward-search, namely date and calendardate.
Try:
QUESTION
I have a dictionary that contains all of the information for company ticker : sector. For example 'AAPL':'Technology'.
I have a CSV file that looks like this:
...ANSWER
Answered 2021-Feb-07 at 07:29- Use
.map, not.applyto select values from adict, by using a column value as akey, because.mapis the method specifically implemented for this operation..mapwill returnNaNif the ticker is not in thedict.
.applycan be used, but.mapshould be useddf['sector'] = df.ticker.apply(lambda x: company_dict.get(x)).getwill returnNoneif the ticker isn't in thedict.
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