My goal is to convert period to datetime.
If Life Was Easy:
master_df = master_df['Month'].to_datetime()
Back Story:
I built a new dataFrame that originally summed the monthly totals and made a 'Month' column by converting a timestamp to period. Now I want to convert that time period back to a timestamp so that I can create plots using matplotlib.
I have tried following:
Reading the docs for Period.to_timestamp.
Converting to a string and then back to datetime. Still keeps the period issue and won't convert.
Following a couple similar questions in Stackoverflow but could not seem to get it to work.
A simple goal would be to plot the following:
plot.bar(m_totals['Month'], m_totals['Showroom Visits']);
This is the error I get if I try to use a period dtype in my charts
ValueError: view limit minimum 0.0 is less than 1 and is an invalid Matplotlib date value.
This often happens if you pass a non-datetime value to an axis that has datetime units.
Additional Material:
Code I used to create the Month column (where period issue was created):
master_df['Month'] = master_df['Entry Date'].dt.to_period('M')
Codes I used to group to monthly totals:
m_sums = master_df.groupby(['DealerName','Month']).sum().drop(columns={'Avg. Response Time','Closing Percent'})
m_means = master_df.groupby(['DealerName','Month']).mean()
m_means = m_means[['Avg. Response Time','Closing Percent']]
m_totals = m_sums.join(m_means)
m_totals.reset_index(inplace=True)
m_totals
Resulting DataFrame:
I was able to cast the period type to string then to datetime. Just could not go straight from period to datetime.
m_totals['Month'] = m_totals['Month'].astype(str)
m_totals['Month'] = pd.to_datetime(m_totals['Month'])
m_totals.dtypes
I wish I did not get downvoted for not providing the entire dataFrame.
First change it to str then to date
index=pd.period_range(start='1949-01',periods=144 ,freq='M')
type(index)
#changing period to date
index=index.astype(str)
index=pd.to_datetime(index)
df.set_index(index,inplace=True)
type(df.index)
df.info()
Another potential solution is to use to_timestamp. For example: m_totals['Month'] = m_totals['Month'].dt.to_timestamp()
Related
Using the ff_monthly.csv data set https://github.com/alexpetralia/fama_french,
use the first column as an index
(this contains the year and month of the data as a string
Create a new column ‘Mkt’ as ‘Mkt-RF’ + ‘RF’
Create two new columns in the loaded DataFrame, ‘Month’ and ‘Year’ to
contain the year and month of the dataset extracted from the index column.
Create a new DataFrame with columns ‘Mean’ and ‘Standard
Deviation’ and the full set of years from (b) above.
Write a function which accepts (r_m,s_m) the monthy mean and standard
deviation of a return series and returns a tuple (r_a,s_a), the annualised
mean and standard deviation. Use the formulae: r_a = (1+r_m)^12 -1, and
s_a = s_m * 12^0.5.
Loop through each year in the data, and calculate the annualised mean and
standard deviation of the new ‘Mkt’ column, storing each in the newly
created DataFrame. Note that the values in the input file are % returns, and
need to be divided by 100 to return decimals (i.e the value for August 2022
represents a return of -3.78%).
. Print the DataFrame and output it to a csv file.
Workings so far:
import pandas as pd
ff_monthly=pd.read_csv(r"file path")
ff_monthly=pd.read_csv(r"file path",index_col=0)
Mkt=ff_monthly['Mkt-RF']+ff_monthly['RF']
ff_monthly= ff_monthly.assign(Mkt=Mkt)
df=pd.DataFrame(ff_monthly)
enter image description here
There are a few things to pay attention to.
The Date is the index of your DataFrame. This is treated in a special way compared to the normal columns. This is the reason df.Date gives an Attribute error. Date is not an Attribute, but the index. Instead try df.index
df.Date.str.split("_", expand=True) would work if your Date would look like 22_10. However according to your picture it doesn't contain an underscore and also contains the day, so this cannot work
In fact the format you have is not even following any standard. In order to properly deal with that the best way would be parsing this to a proper datetime64[ns] type that pandas will understand with df.index = pd.to_datetime(df.index, format='%y%m%d'). See the python docu for supported format strings.
If all this works, it should be rather straightforward to create the columns
df['year'] = df.index.dt.year
In fact, this part has been asked before
I'm trying to pull some data from yfinance in Python for different funds from different exchanges. In pulling my data I just set-up the start and end dates through:
start = '2002-01-01'
end = '2022-06-30'
and pulling it through:
assets = ['GOVT', 'IDNA.L', 'IMEU.L', 'EMMUSA.SW', 'EEM', 'IJPD.L', 'VCIT',
'LQD', 'JNK', 'JNKE.L', 'IEF', 'IEI', 'SHY', 'TLH', 'IGIB',
'IHYG.L', 'TIP', 'TLT']
assets.sort()
data = yf.download(assets, start = start, end = end)
I guess you've noticed that the "assets" or the ETFs come from different exchanges such as ".L" or ".SW".
Now the result this:
It seems to me that there is no overlap for a single instrument (i.e. two prices for the same day). So I don't think the data will be disturbed if any scrubbing or clean-up is done.
So my goal is to harmonize or consolidate the prices to its date index rather than date-and-time index so that each price for each instrument is firmly side-by-side each other for a particular date.
Thanks!
If you want the daily last closing price from the yahoo-finance api you could use the interval argument,
yf.download(assets, start=start, end=end, interval="1d")
Solution with Pandas:
Transforming the Index
You have an index where each row is a string representing the datetime. You firstly want to transform those strings to an actual DatetimeIndex where each row will be of type datetime64. This is done in order to easily work with dates in you dataset applying functions from the datetime library. Finally, you pick the date from each datetime64;
data.index = pd.to_datetime(data.index).date
Groupby
Now that you have an index of dates you can groupby on index. Firstly, you want to deal with NaN values. If you want that the closing price is only considered to fill the values within the date itself only you want to apply:
data= data.groupby(data.index).ffill()
Otherwise, if you think that the closing price of (e.g.) the 1st October can be used not only to filter values in the 1st October but also 2nd and 3rd of October which have NaN values, simply apply the ffill() without the groupby;
data= data.ffill()
Lastly, taking last observed record grouping for date (Index); Note that you can apply all the functions you want here, even a custom lambda;
data = data.groupby(data.index).last()
I have values that are mesured event-related. So there are not the same amount of data every Minute. To be able to better handle this data I aim to only take the first row of values every Minute.
The time of the data I import from a csv looks like this:
time
11.11.2011 11:11
11.11.2011 11:11
11.11.2011 11:11
11.11.2011 11:12
11.11.2011 11:12
11.11.2011 11:13
The other values are Temperatures.
One main problem ist to import the time in the right format.
I tried to solve this with the help of this comunity like this:
with open('my_file.csv','r') as file:
for line in file:
try:
time = line.split(';')[0] #splits the line at the comma and takes the first bit
time = dt.datetime.strptime(time, '%d.%m.%Y %H:%M')
print(time)
except:
pass
then I importet the columns of the temperatures and joind them like this:
df = pd.read_csv("my_file.csv", sep=';', encoding='latin-1')
df=df[["time", "T1", "T2", "DT1", "DT2"]]
when I printed the dtypes of my data the time was datetime64[ns] and the others where objects.
I tried different options of groupby and resample. Like the following:
df=df.groupby([pd.Grouper(key = 'time', freq='1min')])
df.resample('M')
One main problem that was stated in the error messages was that the datatype of the time was not appropriate for grouping,... because it is not an DatetimeIndex.
So I tried to convert the dates to a DatetimeIndex like this:
df.index = pd.to_datetime(daten["time"].index, format='%Y-%m-%d %H:%M:%S')
but then I reseaved a Nummeration of the Index starting with 1970-01-01 so I am not quite shure if this conversion is possible with irregular data.
Without this conversion I also get the message <pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000026938A74850>
When I then try to call my dataframe the message shows and when saving it to csv like this:
df.to_csv('04_01_DTempminuten.csv', index=False, encoding='utf-8', sep =';', date_format = '%Y-%m-%d %H:%M:%S')
I receive either the same message or only one line with a Dezimalnumber instead of the time.
Does anyone have an idear how to deal with this irregular data to get one line of values each minute?
Thank you for reading my question. I am really thankful for any Idears.
Without sample data I can only show how I do it with irregular time series, which I think is your case. I work with price data which comes at irregular time intervals. So if you need to sample taking the first minute value you can use resample with for a specific interval using ohlc aggregation function, that will give you four columns for each sample interval.
open: first value in the interval
high: highest
low: lowest value
close: last value
In your case the sampling interval would 1 minute ('T')
In the following example I'm using one second ('S') as resampling frequency, to resample ask column (your temperature column):
import pandas as pd
df = pd.read_csv('my_tick_data.csv')
df['date_time'] = pd.to_datetime(df['date_time'])
df.set_index('date_time', inplace=True)
df.head(6)
df['ask'].resample('S').ohlc()
This is not solving your date issue, which is a prerequisite for this part because the data set needs to be indexed by date. If you can provide sample data maybe I can help you with that part either.
Python newbie here but I have some data that is intra-day financial data, going back to 2012, so it's got the same hours each day(same trading session each day) but just different dates. I want to be able to select certain times out of the data and check the corresponding OHLC data for that period and then do some analysis on it.
So at the moment it's a CSV file, and I'm doing:
import pandas as pd
data = pd.DataFrame.read_csv('data.csv')
date = data['date']
op = data['open']
high = data['high']
low = data['low']
close = data['close']
volume = data['volume']
The thing is that the date column is in the format of "dd/mm/yyyy 00:00:00 "as one string or whatever, so is it possible to still select between a certain time, like between "09:00:00" and "10:00:00"? or do I have to separate that time bit from the date and make it it's own column? If so, how?
So I believe pandas has a between_time() function, but that seems to need a DataFrame, so how can I convert it to a DataFrame, then I should be able to use the between_time function to select between the times I want. Also because there's obviously thousands of days, all with their own "xx:xx:xx" to "xx:xx:xx" I want to pull that same time period I want to look at from each day, not just the first lot of "xx:xx:xx" to "xx:xx:xx" as it makes its way down the data, if that makes sense. Thanks!!
Consider the dataframe df
from pandas_datareader import data
df = data.get_data_yahoo('AAPL', start='2016-08-01', end='2016-08-03')
df = df.asfreq('H').ffill()
option 1
convert index to series then dt.hour.isin
slc = df.index.to_series().dt.hour.isin([9, 10])
df.loc[slc]
option 2
numpy broadcasting
slc = (df.index.hour[:, None] == [9, 10]).any(1)
df.loc[slc]
response to comment
To then get a range within that time slot per day, use resample + agg + np.ptp (peak to peak)
df.loc[slc].resample('D').agg(np.ptp)
Currently I'm generating a DateTimeIndex using a certain function, zipline.utils.tradingcalendar.get_trading_days. The time series is roughly daily but with some gaps.
My goal is to get the last date in the DateTimeIndex for each month.
.to_period('M') & .to_timestamp('M') don't work since they give the last day of the month rather than the last value of the variable in each month.
As an example, if this is my time series I would want to select '2015-05-29' while the last day of the month is '2015-05-31'.
['2015-05-18', '2015-05-19', '2015-05-20', '2015-05-21',
'2015-05-22', '2015-05-26', '2015-05-27', '2015-05-28',
'2015-05-29', '2015-06-01']
Condla's answer came closest to what I needed except that since my time index stretched for more than a year I needed to groupby by both month and year and then select the maximum date. Below is the code I ended up with.
# tempTradeDays is the initial DatetimeIndex
dateRange = []
tempYear = None
dictYears = tempTradeDays.groupby(tempTradeDays.year)
for yr in dictYears.keys():
tempYear = pd.DatetimeIndex(dictYears[yr]).groupby(pd.DatetimeIndex(dictYears[yr]).month)
for m in tempYear.keys():
dateRange.append(max(tempYear[m]))
dateRange = pd.DatetimeIndex(dateRange).order()
Suppose your data frame looks like this
original dataframe
Then the following Code will give you the last day of each month.
df_monthly = df.reset_index().groupby([df.index.year,df.index.month],as_index=False).last().set_index('index')
transformed_dataframe
This one line code does its job :)
My strategy would be to group by month and then select the "maximum" of each group:
If "dt" is your DatetimeIndex object:
last_dates_of_the_month = []
dt_month_group_dict = dt.groupby(dt.month)
for month in dt_month_group_dict:
last_date = max(dt_month_group_dict[month])
last_dates_of_the_month.append(last_date)
The list "last_date_of_the_month" contains all occuring last dates of each month in your dataset. You can use this list to create a DatetimeIndex in pandas again (or whatever you want to do with it).
This is an old question, but all existing answers here aren't perfect. This is the solution I came up with (assuming that date is a sorted index), which can be even written in one line, but I split it for readability:
month1 = pd.Series(apple.index.month)
month2 = pd.Series(apple.index.month).shift(-1)
mask = (month1 != month2)
apple[mask.values].head(10)
Few notes here:
Shifting a datetime series requires another pd.Series instance (see here)
Boolean mask indexing requires .values (see here)
By the way, when the dates are the business days, it'd be easier to use resampling: apple.resample('BM')
Maybe the answer is not needed anymore, but while searching for an answer to the same question I found maybe a simpler solution:
import pandas as pd
sample_dates = pd.date_range(start='2010-01-01', periods=100, freq='B')
month_end_dates = sample_dates[sample_dates.is_month_end]
Try this, to create a new diff column where the value 1 points to the change from one month to the next.
df['diff'] = np.where(df['Date'].dt.month.diff() != 0,1,0)