I'm starting from the pandas DataFrame docs here: http://pandas.pydata.org/pandas-docs/stable/dsintro.html
I'd like to iteratively fill the DataFrame with values in a time series kind of calculation. So basically, I'd like to initialize the DataFrame with columns A, B and timestamp rows, all 0 or all NaN.
I'd then add initial values and go over this data calculating the new row from the row before, say row[A][t] = row[A][t-1]+1
or so.
I'm currently using the code as below, but I feel it's kind of ugly and there must be a way to do this with a DataFrame directly, or just a better way in general. Note: I'm using Python 2.7.
import datetime as dt
import pandas as pd
import scipy as s
if __name__ == '__main__':
base = dt.datetime.today().date()
dates = [ base - dt.timedelta(days=x) for x in range(0,10) ]
dates.sort()
valdict = {}
symbols = ['A','B', 'C']
for symb in symbols:
valdict[symb] = pd.Series( s.zeros( len(dates)), dates )
for thedate in dates:
if thedate > dates[0]:
for symb in valdict:
valdict[symb][thedate] = 1+valdict[symb][thedate - dt.timedelta(days=1)]
print valdict
Here's a couple of suggestions:
Use date_range
for the index:
import datetime
import pandas as pd
import numpy as np
todays_date = datetime.datetime.now().date()
index = pd.date_range(todays_date-datetime.timedelta(10), periods=10, freq='D')
columns = ['A','B', 'C']
Note: we could create an empty DataFrame (with NaN
s) simply by writing:
df_ = pd.DataFrame(index=index, columns=columns)
df_ = df_.fillna(0) # with 0s rather than NaNs
To do these type of calculations for the data, use a numpy array:
data = np.array([np.arange(10)]*3).T
Hence we can create the DataFrame:
In [10]: df = pd.DataFrame(data, index=index, columns=columns)
In [11]: df
Out[11]:
A B C
2012-11-29 0 0 0
2012-11-30 1 1 1
2012-12-01 2 2 2
2012-12-02 3 3 3
2012-12-03 4 4 4
2012-12-04 5 5 5
2012-12-05 6 6 6
2012-12-06 7 7 7
2012-12-07 8 8 8
2012-12-08 9 9 9