Take the following code:
import MySQLdb as mdb
import pandas as pd
con = mdb.connect(db_host, db_user, db_pass, db_name)
query = """SELECT `TIME`.`BID-CLOSE`
FROM `EUR-USD`.`tbl_EUR-USD_1-Day`
WHERE TIME >= '2006-12-15 22:00:00' AND TIME <= '2007-01-03 22:00:00'
ORDER BY TIME ASC;"""
# Create a pandas dataframe from the SQL query
eurusd = pd.read_sql_query(query, con=con, index_col='TIME')
idx = pd.date_range('2006-12-17 22:00:00', '2007-01-03 22:00:00')
eurusd.reindex(idx, fill_value=None)
This gives an output of
BID-CLOSE
2006-12-17 22:00:00 1.30971
2006-12-18 22:00:00 1.31971
2006-12-19 22:00:00 1.31721
2006-12-20 22:00:00 1.31771
2006-12-21 22:00:00 1.31411
2006-12-22 22:00:00 NaN
2006-12-23 22:00:00 NaN
2006-12-24 22:00:00 NaN
2006-12-25 22:00:00 1.30971
2006-12-26 22:00:00 1.31131
2006-12-27 22:00:00 1.31491
2006-12-28 22:00:00 1.32021
2006-12-29 22:00:00 NaN
2006-12-30 22:00:00 NaN
2006-12-31 22:00:00 1.32731
2007-01-01 22:00:00 1.32731
2007-01-02 22:00:00 1.31701
2007-01-03 22:00:00 1.30831
Re-Index the data
eurusd = eurusd.reindex(idx, fill_value=None)
List of interpolate types
methods = ['linear', 'quadratic', 'cubic']
Next line throws an Exception...
pd.DataFrame({m: eurusd.interpolate(method=m) for m in methods})
ValueError: If using all scalar values, you must pass an index
Following the Interpolation section of this guide http://pandas.pydata.org/pandas-docs/stable/missing_data.html How do I correctly 'pass an index' in this situation?
Update 1
The output of eurusd.interpolate('linear')
BID-CLOSE
2006-12-17 22:00:00 1.309710
2006-12-18 22:00:00 1.319710
2006-12-19 22:00:00 1.317210
2006-12-20 22:00:00 1.317710
2006-12-21 22:00:00 1.314110
2006-12-22 22:00:00 1.313010
2006-12-23 22:00:00 1.311910
2006-12-24 22:00:00 1.310810
2006-12-25 22:00:00 1.309710
2006-12-26 22:00:00 1.311310
2006-12-27 22:00:00 1.314910
2006-12-28 22:00:00 1.320210
2006-12-29 22:00:00 1.322577
2006-12-30 22:00:00 1.324943
2006-12-31 22:00:00 1.327310
2007-01-01 22:00:00 1.327310
2007-01-02 22:00:00 1.317010
2007-01-03 22:00:00 1.308310
Update 2
In[9]: pd.DataFrame({m: eurusd['BID-CLOSE'].interpolate(method=m) for m in methods})
Out[9]:
cubic linear quadratic
2006-12-17 22:00:00 1.309710 1.309710 1.309710
2006-12-18 22:00:00 1.319710 1.319710 1.319710
2006-12-19 22:00:00 1.317210 1.317210 1.317210
2006-12-20 22:00:00 1.317710 1.317710 1.317710
2006-12-21 22:00:00 1.314110 1.314110 1.314110
2006-12-22 22:00:00 1.310762 1.313010 1.307947
2006-12-23 22:00:00 1.309191 1.311910 1.305159
2006-12-24 22:00:00 1.308980 1.310810 1.305747
2006-12-25 22:00:00 1.309710 1.309710 1.309710
2006-12-26 22:00:00 1.311310 1.311310 1.311310
2006-12-27 22:00:00 1.314910 1.314910 1.314910
2006-12-28 22:00:00 1.320210 1.320210 1.320210
2006-12-29 22:00:00 1.323674 1.322577 1.321632
2006-12-30 22:00:00 1.325553 1.324943 1.323998
2006-12-31 22:00:00 1.327310 1.327310 1.327310
2007-01-01 22:00:00 1.327310 1.327310 1.327310
2007-01-02 22:00:00 1.317010 1.317010 1.317010
2007-01-03 22:00:00 1.308310 1.308310 1.308310
The problem is that when you use the DataFrame constructor:
pd.DataFrame({m: eurusd.interpolate(method=m) for m in methods})
the value for each m is a DataFrame, which will be interpreted as a scalar value, which is admittedly confusing. This constructer expects some sort of sequence or Series. The following should solve the problem:
pd.DataFrame({m: eurusd['BID-CLOSE'].interpolate(method=m) for m in methods})
Since subsetting on a column returns a Series. So, for example instead of:
In [34]: pd.DataFrame({'linear':df.interpolate('linear')})
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-34-4b6c095c6da3> in <module>()
----> 1 pd.DataFrame({'linear':df.interpolate('linear')})
/home/juan/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
222 dtype=dtype, copy=copy)
223 elif isinstance(data, dict):
--> 224 mgr = self._init_dict(data, index, columns, dtype=dtype)
225 elif isinstance(data, ma.MaskedArray):
226 import numpy.ma.mrecords as mrecords
/home/juan/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py in _init_dict(self, data, index, columns, dtype)
358 arrays = [data[k] for k in keys]
359
--> 360 return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
361
362 def _init_ndarray(self, values, index, columns, dtype=None, copy=False):
/home/juan/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
5229 # figure out the index, if necessary
5230 if index is None:
-> 5231 index = extract_index(arrays)
5232 else:
5233 index = _ensure_index(index)
/home/juan/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py in extract_index(data)
5268
5269 if not indexes and not raw_lengths:
-> 5270 raise ValueError('If using all scalar values, you must pass'
5271 ' an index')
5272
ValueError: If using all scalar values, you must pass an index
Use this instead:
In [35]: pd.DataFrame({'linear':df['BID-CLOSE'].interpolate('linear')})
Out[35]:
linear
timestamp
2016-10-10 22:00:00 1.309710
2016-10-10 22:00:00 1.319710
2016-10-10 22:00:00 1.317210
2016-10-10 22:00:00 1.317710
2016-10-10 22:00:00 1.314110
2016-10-10 22:00:00 1.313010
2016-10-10 22:00:00 1.311910
2016-10-10 22:00:00 1.310810
2016-10-10 22:00:00 1.309710
2016-10-10 22:00:00 1.311310
2016-10-10 22:00:00 1.314910
2016-10-10 22:00:00 1.320210
2016-10-10 22:00:00 1.322577
2016-10-10 22:00:00 1.324943
2016-10-10 22:00:00 1.327310
2016-10-10 22:00:00 1.327310
2016-10-10 22:00:00 1.317010
2016-10-10 22:00:00 1.308310
Fair warning, though, I am getting a LinAlgError: singular matrix error when I try 'quadratic' and 'cubic' interpolation on your data. Not sure why though.