My goal:
I have two time-series data frames, one with a time interval of 1m and the other with a time interval of 5m. The 5m data frame is a resampled version of the 1m data. What I'm doing is computing a set of RSI values that correspond to the 5m df using the vectorbt library, then aligning and broadcasting these values to the 1m df using df.align
The Problem:
When trying to do this line by line, it works perfectly. Here's what the final result looks like:
However, when applying it under the function, it returns the following error while having overlapping index names:
ValueError: cannot join with no overlapping index names
Here's the complete code:
import vectorbt as vbt
import numpy as np
import pandas as pd
import datetime
end_date = datetime.datetime.now()
start_date = end_date - datetime.timedelta(days=3)
btc_price = vbt.YFData.download('BTC-USD',
interval='1m',
start=start_date,
end=end_date,
missing_index='drop').get('Close')
def custom_indicator(close, rsi_window=14, ma_window=50):
close_5m = close.resample('5T').last()
rsi = vbt.RSI.run(close_5m, window=rsi_window).rsi
rsi, _ = rsi.align(close, broadcast_axis=0, method='ffill')
print(rsi) #to check
print(close) #to check
return
#setting up indicator factory
ind = vbt.IndicatorFactory(
class_name='Combination',
short_name='comb',
input_names=['close'],
param_names=['rsi_window', 'ma_window'],
output_names=['value']).from_apply_func(custom_indicator,
rsi_window=14,
ma_window=50,
keep_pd=True)
res = ind.run(btc_price, rsi_window=21, ma_window=50)
print(res)
Thank you for taking the time to read this. Any help would be appreciated!
if you checked the columns of both , rsi and close
print('close is', close.columns)
print('rsi is', rsi.columns)
you will find
rsi is MultiIndex([(14, 'Close')],
names=['rsi_window', None])
close is Index(['Close'], dtype='object')
as it has two indexes, one should be dropped, so it can be done by the below code
rsi.columns = rsi.columns.droplevel()
to drop one level of the indexes, so it could be align,
The problem is that the data must be a time series and not a pandas data frame for table joins using align
You need to fix the data type
# Time Series
close = close['Close']
close_5m = close.resample('15min').last()
rsi = vbt.RSI.run(close_5m, window=rsi_window).rsi
rsi, _ = rsi.align(close, broadcast_axis=0, method='ffill', join='right')
When you are aligning the data make sure to include join='right'
rsi, _ = rsi.align(close, broadcast_axis=0, method='ffill', join='right'
I have a table like this (with more columns):
date,Sector,Value1,Value2
14/03/22,Medical,86,64
14/03/22,Medical,464,99
14/03/22,Industry,22,35
14/03/22,Services,555,843
15/03/22,Services,111,533
15/03/22,Industry,222,169
15/03/22,Medical,672,937
15/03/22,Medical,5534,825
I have created some features like this:
sectorGroup = df.groupby(["date","Sector"])["Value1","Value2"].mean().reset_index()
df = pd.merge(df,sectorGroup,on=["date","Sector"],how="left",suffixes=["","_bySector"])
dateGroupGroup = df.groupby(["date"])["Value1","Value2"].mean().reset_index()
df = pd.merge(df,dateGroupGroup,on=["date"],how="left",suffixes=["","_byDate"])
Now my new df looks like this:
date,Sector,Value1,Value2,Value1_bySector,Value2_bySector,Value1_byDate,Value2_byDate
14/03/22,Medical,86,64,275.0,81.5,281.75,260.25
14/03/22,Medical,464,99,275.0,81.5,281.75,260.25
14/03/22,Industry,22,35,22.0,35.0,281.75,260.25
14/03/22,Services,555,843,555.0,843.0,281.75,260.25
15/03/22,Services,111,533,111.0,533.0,1634.75,616.0
15/03/22,Industry,222,169,222.0,169.0,1634.75,616.0
15/03/22,Medical,672,937,3103.0,881.0,1634.75,616.0
15/03/22,Medical,5534,825,3103.0,881.0,1634.75,616.0
Now, I want to create lag features for Value1_bySector,Value2_bySector,Value1_byDate,Value2_byDate
For example, a new column named Value1_by_Date_lag1 and Value1_bySector_lag1.
And this new column will look like this:
date,Sector,Value1_by_Date_lag1,Value1_bySector_lag1
15/03/22,Services,281.75,555.0
15/03/22,Industry,281.75,22.0
15/03/22,Medical,281.75,275.0
15/03/22,Medical,281.75,275.0
Basically in Value1_by_Date_lag1, the date "15/03" will contain the value "281.75" which is for the date "14/03" (lag of 1 shift).
Basically in Value1_bySector_lag1, the date "15/03" and Sector "Medical" will contain the value "275.0", which is the value for "14/03" and "Medical" rows.
I hope, the question is clear and gave you all the details.
Create a lagged date variable by shifting the date column, and then merge again with dateGroupGroup and sectorGroup using the lagged date instead of the actual date.
df = pd.read_csv(io.StringIO("""date,Sector,Value1,Value2
14/03/22,Medical,86,64
14/03/22,Medical,464,99
14/03/22,Industry,22,35
14/03/22,Services,555,843
15/03/22,Services,111,533
15/03/22,Industry,222,169
15/03/22,Medical,672,937
15/03/22,Medical,5534,825"""))
# Add a lagged date variable
lagged = df.groupby("date")["date"].first().shift()
df = df.join(lagged, on="date", rsuffix="_lag")
# Create date and sector groups and merge them into df, as you already do
sectorGroup = df.groupby(["date","Sector"])[["Value1","Value2"]].mean().reset_index()
df = pd.merge(df,sectorGroup,on=["date","Sector"],how="left",suffixes=["","_bySector"])
dateGroupGroup = df.groupby("date")[["Value1","Value2"]].mean().reset_index()
df = pd.merge(df, dateGroupGroup, on="date",how="left", suffixes=["","_byDate"])
# Merge again, this time matching the lagged date in df to the actual date in sectorGroup and dateGroupGroup
df = pd.merge(df, sectorGroup, left_on=["date_lag", "Sector"], right_on=["date", "Sector"], how="left", suffixes=["", "_by_sector_lag"])
df = pd.merge(df, dateGroupGroup, left_on="date_lag", right_on="date", how="left", suffixes=["", "_by_date_lag"])
# Drop the extra unnecessary columns that have been created in the merge
df = df.drop(columns=['date_by_date_lag', 'date_by_sector_lag'])
This assumes the data is sorted by date - if not you will have to sort before generating the lagged date. It will work whether or not all the dates are consecutive.
I found 1 inefficient solution (slow and memory intensive).
Lag of "date" group
cols = ["Value1_byDate","Value2_byDate"]
temp = df[["date"]+cols]
temp = temp.drop_duplicates()
for i in range(10):
temp.date = temp.date.shift(-1-i)
df = pd.merge(df,temp,on="date",how="left",suffixes=["","_lag"+str(i+1)])
Lag of "date" and "Sector" group
cols = ["Value1_bySector","Value2_bySector"]
temp = df[["date","Sector"]+cols]
temp = temp.drop_duplicates()
for i in range(10):
temp[["Value1_bySector","Value2_bySector"]] = temp.groupby("Sector")["Value1_bySector","Value2_bySector"].shift(1+1)
df = pd.merge(df,temp,on=["date","Sector"],how="left",suffixes=["","_lag"+str(i+1)])
Is there a more simple solution?
I am trying to manipulate a CSV file on a certain date in a certain column.
I am using pandas (total noob) for that and was pretty successful until i got to dates.
The CSV looks something like this (with more columns and rows of course).
These are the columns:
Circuit
Status
Effective Date
These are the values:
XXXX001
Operational
31-DEC-2007
I tried dataframe query (which i use for everything else) without success.
I tried dataframe loc (which worked for everything else) without success.
How can i get all rows that are older or newer from a given date? If i have other conditions to filter the dataframe, how do i combine them with the date filter?
Here's my "raw" code:
import pandas as pd
# parse_dates = ['Effective Date']
# dtypes = {'Effective Date': 'str'}
df = pd.read_csv("example.csv", dtype=object)
# , parse_dates=parse_dates, infer_datetime_format=True
# tried lot of suggestions found on SO
cols = df.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
df.columns = cols
status1 = 'Suppressed'
status2 = 'Order Aborted'
pool = '2'
region = 'EU'
date1 = '31-DEC-2017'
filt_df = df.query('Status != #status1 and Status != #status2 and Pool == #pool and Region_A == #region')
filt_df.reset_index(drop=True, inplace=True)
filt_df.to_csv('filtered.csv')
# this is working pretty well
supp_df = df.query('Status == #status1 and Effective_Date < #date1')
supp_df.reset_index(drop=True, inplace=True)
supp_df.to_csv('supp.csv')
# this is what is not working at all
I tried many approaches, but i was not able to put it together. This is just one of many approaches i tried.. so i know it is perhaps completely wrong, as no date parsing is used.
supp.csv will be saved, but the dates present are all over the place, so there's no match with the "logic" in this code.
Thanks for any help!
Make sure you convert your date to datetime and then filter slice on it.
df['Effective Date'] = pd.to_datetime(df['Effective Date'])
df[df['Effective Date'] < '2017-12-31']
#This returns all the values with dates before 31th of December, 2017.
#You can also use Query
I am iterating over the rows that are available, but it doesn't seem to be the most optimal way to do it -- it takes forever.
Is there a special way in Pandas to do it.
INIT_TIME = datetime.datetime.strptime(date + ' ' + time, "%Y-%B-%d %H:%M:%S")
#NEED TO ADD DATA FROM THAT COLUMN
df = pd.read_csv(dataset_path, delimiter=',',skiprows=range(0,1),names=['TCOUNT','CORE','COUNTER','EMPTY','NAME','TSTAMP','MULT','STAMPME'])
df = df.drop('MULT',1)
df = df.drop('EMPTY',1)
df = df.drop('TSTAMP', 1)
for index, row in df.iterrows():
TMP_TIME = INIT_TIME + datetime.timedelta(seconds=row['TCOUNT'])
df['STAMPME'] = TMP_TIME.strftime("%s")
In addition, the datetime I am adding is in the following format
2017-05-11 11:12:37.100192 1494493957
2017-05-11 11:12:37.200541 1494493957
and therefore the unix timestamp is same (and it is correct), but is there a better way to represent it?
Assuming the datetimes are correctly reflecting what you're trying to do, with respect to Pandas you should be able to do:
df['STAMPME'] = df['TCOUNT'].apply(lambda x: (datetime.timedelta(seconds=x) + INIT_TIME).strftime("%s"))
As noted here you should not use iterrows() to modify the DF you are iterating over. If you need to iterate row by row (as opposed to using the apply method) you can use another data object, e.g. a list, to retain the values you're calculating, and then create a new column from that.
Also, for future reference, the itertuples() method is faster than iterrows(), although it requires you to know the index of each column (i.e. row[x] as opposed to row['name']).
I'd rewrite your code like this
INIT_TIME = datetime.datetime.strptime(date + ' ' + time, "%Y-%B-%d %H:%M:%S")
INIT_TIME = pd.to_datetime(INIT_TIME)
df = pd.read_csv(
dataset_path, delimiter=',',skiprows=range(0,1),
names=['TCOUNT','CORE','COUNTER','EMPTY','NAME','TSTAMP','MULT','STAMPME']
)
df = df.drop(['MULT', 'EMPTY', 'TSTAMP'], 1)
df['STAMPME'] = pd.to_timedelta(df['TCOUNT'], 's') + INIT_TIME