Key Error while subsetting Timeseries data using index - python-3.x

Question

I have the following Timeseries data.
price_per_year.head()
price
date
2013-01-02 20.08
2013-01-03 19.78
2013-01-04 19.86
2013-01-07 19.40
2013-01-08 19.66
price_per_year.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 782 entries, 2013-01-02 to 2015-12-31
Data columns (total 1 columns):
price 756 non-null float64
dtypes: float64(1)
memory usage: 12.2 KB
I am trying to extract data for 3 years using the below code. Why is that I am getting KeyError: '2014', when the data as shown below clearly contains year '2014'. Appreciate any inputs.
price_per_year['2014'].head()
price
date
2014-01-01 NaN
2014-01-02 39.59
2014-01-03 40.12
2014-01-06 39.93
2014-01-07 40.92
prices = pd.DataFrame()
for year in ['2013', '2014', '2015']:
price_per_year = price_per_year.loc[year, ['price']].reset_index(drop=True)
price_per_year.rename(columns={'price': year}, inplace=True)
prices = pd.concat([prices, price_per_year], axis=1)
KeyError: '2014'
The code line price_per_year.loc['2014', ['price']], when used independently outside for loop, works fine, while price_per_year['price'][year] when used in the for loop doesn't work.
for year in ['2013', '2014', '2015']:
price_per_year = price_per_year['price'][year].reset_index(drop=True)
KeyError: 'price'
Both the code lines price_per_year.loc[price_per_year.index.year == 2014, ['price']] when used independently outside for loop and price_per_year.loc[price_per_year.index.year == year, ['price']] used inside the for loop are giving errors.
for year in ['2013', '2014', '2015']:
price_per_year.loc[price_per_year.index.year == '2014', ['price']].reset_index(drop=True)
TypeError: Cannot convert input [False] of type <class 'bool'> to Timestamp

Answer 1

Here is problem in your first code is used partial string indexing, here is used DataFrame.loc
prices = pd.DataFrame()
for year in ['2013', '2014', '2015']:
s = price_per_year['price'][year].reset_index(drop=True).rename(year)
prices = pd.concat([prices, s], axis=1)
print (prices)
2013 2014 2015
0 20.08 19.86 19.66
1 19.78 19.40 19.66
Another better solution with reshape:
print (df)
price
date
2013-01-02 20.08
2013-01-03 19.78
2014-01-02 19.86
2014-01-03 19.40
2015-01-02 19.66
2015-01-03 19.66
y = df.index.year
df = df.set_index([df.groupby(y).cumcount(), y])['price'].unstack()
print (df)
date 2013 2014 2015
0 20.08 19.86 19.66
1 19.78 19.40 19.66