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pandapy

PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

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PandaPy

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DOI

"I came across PandaPy last week and have already used it in my current project. It is a fascinating Python library with a lot of potential to become mainstream."

SSRN Report

Snow, Derek (2020), PandaPy: A Wrapper Around Structured Arrays to Mimic ‘structs’ in the C Language, SSRN

@software{pandapy,
  title = {{PandaPy}: A Wrapper Around Structured Arrays to Mimic ‘structs’ in the C Language.},
  author = {Snow, Derek},
  url = {https://github.com/firmai/pandapy/},
  version = {1.11},
  date = {2020-05-13},
}

Animated investment research at Sov.ai, sponsoring open source initiatives.


Install

!pip3 install pandapy

Load

import pandapy as pp

Why PandaPy?

  1. Maintains the full functionality and speed of structured NumPy datatype (eg., array[col1] + array[col2], or np.log(array[col1])
  2. If you have smaller pandas dataframes (<50K number of records) in a production environment, then it is worth considering PandaPy, you will see a significant speed up and a large reduction in memory usage.
  3. When using mixed data types (int, float, datatime, str), PandaPy generally consumes (roughly a 1/3rd) less memory than Pandas.
  4. Pandas outperform PandaPy at the same point when Pandas outperform NumPy. NumPy generally performs better than pandas for 50K rows or less. Pandas generally performs better than numpy for 500K rows or more; from 50K to 500K rows it is a toss up depending on the operation.
  5. Because both Pandas and PandaPy is built on NumPy, the performance difference can be attributed to Pandas overhead. For larger datasets Pandas' hash tables and columnar data format gives it the upperhand on many operations.
  6. The performance claims therefore hold for small datasets, 1,000-100,000 numpy rows. There is however many PandaPy operations that improve relative to Pandas as the number of rows increase: rename, column drop, fillna mean, correlation matrix, filter (array > 0), value reads(a=array[col]), singular value access (array[col][pos]), atomic functions (sqrt, power), and np. calculations differences even out (np.log, np.exp, etc).
  7. Provides wrapper functions over NumPy to give you the usability of Pandas (eg., pp.group(array, [col1, col2, col2], ['mean', 'std'], ['Adj_Close','Close'])
  8. If you need Pandas for speciality functions, you can easily df = pp.pandas(array) and back array = pp.structured(df)
  9. For simple calculations on a small dataset (i.e, plus, mult, log) PandaPy is 25x - 80x faster than Pandas.
  10. For table functions (i.e., group, pivot, drop, concat, fillna) on a small data set PandaPy is 5x - 100x times faster than Pandas.
  11. For most use cases with small data, PandaPy is faster than Dask, Modin Ray and Pandas.
  12. The best competing python package for performance on table functions is datatable, it is 2x - 10x faster than PandaPy.
  13. The problem is that datatable is 5x - 10x slower with simple calculations (plus, mult, returns), it is less intuitive, does not have a large range of functions, have very few complementary libraries, e.g. matplotlib, and doesn't leave you in a Numpy datatype.
  14. For finance applications the speed of simple calculations takes preference over table function speed.
  15. PandaPy is not created to allow you to scale up to clusters for multiple computer processing like Dask, Modin, and Spark, instead it is focused on speed and usability within a single computer's Memory.
  16. Machines are getting large, EC2 X1 has 2TB of RAM and is remarkably affordable. If it can be done on a single machine then it should be done on a single machine. Quoting Dask - "For data that fits into RAM, Pandas {PandaPy, NumPy} can often be faster and easier to use than Dask DataFrame"
  17. If your dataset is very small you can load your data using PandaPy's read() function, for medium sized data, it is best to load it with datatable or pyspark and convert it to structured Numpy, if it is large, pyspark, Dask, or Modin, if it is very large use pyspark.
  18. Lastly PandaPy can have as input any multidimensional object and does not have to conform to the basic NumPy datatypes. It can include nested datatypes, subarrays, functions as long as each column conforms to the array lenght, this allows for a great amount of flexibility. You can for example, add(array, "panda function",[[pd for i in range(len(multiple_stocks))]]) to create a list of the panda (pd) module and access it along any index value array["panda function"][0].read_csv(url).

PandaPy software, similar to the original Pandas project, is developed to improve the usability of python for finance. Structured datatypes are designed to be able to mimic ‘structs’ in the C language, and share a similar memory layout. PandaPy currently houses more than 30 functions. Structured NumPy are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.

Note this is a fledgling project, much room for improvement, all feedback appreciated (issues tab)

Description


A Structured NumPy Array is an array of structures. NumPy arrays can only contain one data type, but structured arrays in a sense create an array of homogeneous structures. This is done without moving out of NumPy such as is required with Xarray. For structured arrays the data type only has to be the same per column like an SQL data base. Each column can be another multidimensional object and does not have to conform to the basic NumPy datatypes.

PandaPy comes with similar functionality like Pandas, such as groupby, pivot, and others. The biggest benefit of this approach is that NumPy dtype(data type) directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program. If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, you'll probably find structured arrays quite useful.

Additional

  1. Play around with speed tests here and some more here.
  2. Test and explore the package with this Google Colab Notebook.
  3. Get in touch on LinkedIn or Twitter.
  4. Use table(array) to get a pandas looking table printout
  5. You can read the paper on SSRN for a little more information.

Functions

PandaPy Speed Over Pandas In (X) e.g., (dropnarow) (30x)


Array Structure

Read In Arrays (read)
To Pandas (unstructured) 
Pandas to Structured (structured) 
To Unstructured (to_unstruct) 
To Structured (to_struct) 
Print Table (table) 

Explorative Functions

Descriptive Statistics (describe) (5x)
Correlation Array (corr) (2x)

Finance Functions

Returns (returns) (50x)  
Portfolio Value (portfolio_value) (50x)
Cummulative Value (cummulative_return) (50x)
Column Lags (lags) (7x)

Array Functions

Drop Null Rows (dropnarow) (30x)
Drop Column/s (drop) (100x)
Add Column/s (add) (3x)
Concatenate (concat) (rows 25x columns 70x)
Merge (merge) (2x)
Group by (group) (10x)
Pivot (pivot) (20x)
Fill Nulls (fillna) (20x)
Shift Column (shift) (50x)
Rename (rename) (500x)

Other Speed Tests

Update (array[col] = values) (60x)
Addition (array[col] + array[col]) (80x)
Multiplication (array[col] * array[col]) (80x)
Log (np.log(array[col]) (25x)

note speed tests done on financial dataset only

Documentation by Example


Read In Arrays

# First Example
multiple_stocks = pp.read('https://github.com/firmai/random-assets-two/blob/master/numpy/multiple_stocks.csv?raw=true')
closing = multiple_stocks[['Ticker','Date','Adj_Close']]
piv = pp.pivot(closing,"Date","Ticker","Adj_Close"); piv
closing = pp.to_struct(piv, name_list = [x for x in np.unique(multiple_stocks["Ticker"])])

# Second Example
tsla = pp.read('https://github.com/firmai/random-assets-two/raw/master/numpy/tsla.csv')
crm = pp.read('https://github.com/firmai/random-assets-two/raw/master/numpy/crm.csv')
tsla_sub = tsla[["Date","Adj_Close","Volume"]]
crm_sub = crm[["Date","Adj_Close","Volume"]]
crm_adj = crm[['Date','Adj_Close']]
closing
array([(37.24206924, 100.45429993, 44.57522202, 20.72605705, 130.59109497, 35.80251312,  41.9791832 ,  81.51140594, 66.33999634),
       (35.08446503,  97.62433624, 43.83200836, 20.34561157, 128.53627014, 35.80251312,  41.59314346,  80.89860535, 66.15000153),
       (35.34244537,  97.63354492, 42.79874039, 19.90727234, 125.76422119, 36.07437897,  40.98268127,  80.28580475, 64.58000183),
       ...,
       (21.57999992, 289.79998779, 59.08000183, 11.18000031, 135.27000427, 55.34999847, 158.96000671, 137.53999329, 88.37000275),
       (21.34000015, 291.51998901, 58.65999985, 11.07999992, 132.80999756, 55.27000046, 157.58999634, 136.80999756, 87.95999908),
       (21.51000023, 293.6499939 , 58.47999954, 11.15999985, 134.03999329, 55.34999847, 157.69999695, 136.66999817, 88.08999634)],
      dtype=[('AA', '<f8'), ('AAPL', '<f8'), ('DAL', '<f8'), ('GE', '<f8'), ('IBM', '<f8'), ('KO', '<f8'), ('MSFT', '<f8'), ('PEP', '<f8'), ('UAL', '<f8')])

Rename

pp.rename(closing,["AA","AAPL"],["GAP","FAF"])[:5]
array([(37.24206924, 100.45429993, 44.57522202, 20.72605705, 130.59109497, 35.80251312, 41.9791832 , 81.51140594, 66.33999634),
       (35.08446503,  97.62433624, 43.83200836, 20.34561157, 128.53627014, 35.80251312, 41.59314346, 80.89860535, 66.15000153),
       (35.34244537,  97.63354492, 42.79874039, 19.90727234, 125.76422119, 36.07437897, 40.98268127, 80.28580475, 64.58000183),
       (36.25707626,  99.00255585, 42.57216263, 19.91554451, 124.94229126, 36.52467346, 41.50337982, 82.63342285, 65.52999878),
       (37.28897095, 102.80648041, 43.67792892, 20.15538216, 127.65791321, 36.966465  , 42.72432327, 84.13523865, 66.63999939)],
      dtype=[('GAP', '<f8'), ('FAF', '<f8'), ('DAL', '<f8'), ('GE', '<f8'), ('IBM', '<f8'), ('KO', '<f8'), ('MSFT', '<f8'), ('PEP', '<f8'), ('UAL', '<f8')])
pp.rename(closing,"AA", "GALLY")[:5]
array([(37.24206924, 100.45429993, 44.57522202, 20.72605705, 130.59109497, 35.80251312, 41.9791832 , 81.51140594, 66.33999634),
       (35.08446503,  97.62433624, 43.83200836, 20.34561157, 128.53627014, 35.80251312, 41.59314346, 80.89860535, 66.15000153),
       (35.34244537,  97.63354492, 42.79874039, 19.90727234, 125.76422119, 36.07437897, 40.98268127, 80.28580475, 64.58000183),
       (36.25707626,  99.00255585, 42.57216263, 19.91554451, 124.94229126, 36.52467346, 41.50337982, 82.63342285, 65.52999878),
       (37.28897095, 102.80648041, 43.67792892, 20.15538216, 127.65791321, 36.966465  , 42.72432327, 84.13523865, 66.63999939)],
      dtype=[('GALLY', '<f8'), ('AAPL', '<f8'), ('DAL', '<f8'), ('GE', '<f8'), ('IBM', '<f8'), ('KO', '<f8'), ('MSFT', '<f8'), ('PEP', '<f8'), ('UAL', '<f8')])

Statistics

described = pp.describe(closing)
Describeobservationsminimummaximummeanvarianceskewnesskurtosis
AA1258.0015.9760.2331.4699.420.67-0.58
AAPL1258.0085.39293.65149.452119.860.66-0.28
DAL1258.0030.7362.6947.1544.33-0.01-0.78
GE1258.006.4228.6718.8548.45-0.25-1.54
IBM1258.0099.83161.17133.35116.28-0.370.56
KO1258.0032.8155.3541.6728.860.80-0.05
MSFT1258.0036.27158.9678.311102.210.61-0.82
PEP1258.0078.46139.30102.86229.010.63-0.32
UAL1258.0037.7596.7069.22195.650.02-1.04

Drop Column/s

removed = pp.drop(closing,["AA","AAPL","IBM"]) ; removed[:5]
array([(44.57522202, 20.72605705, 35.80251312, 41.9791832 , 81.51140594, 66.33999634),
       (43.83200836, 20.34561157, 35.80251312, 41.59314346, 80.89860535, 66.15000153),
       (42.79874039, 19.90727234, 36.07437897, 40.98268127, 80.28580475, 64.58000183),
       (42.57216263, 19.91554451, 36.52467346, 41.50337982, 82.63342285, 65.52999878),
       (43.67792892, 20.15538216, 36.966465  , 42.72432327, 84.13523865, 66.63999939)],
      dtype={'names':['DAL','GE','KO','MSFT','PEP','UAL'], 'formats':['<f8','<f8','<f8','<f8','<f8','<f8'], 'offsets':[16,24,40,48,56,64], 'itemsize':72})

Add Column/s

added = pp.add(closing,["GALLY","FAF"],[closing["IBM"],closing["AA"]]); added[:5]  ## set two new columns with that two previous columnns
array([(37.24206924, 100.45429993, 44.57522202, 20.72605705, 130.59109497, 35.80251312, 41.9791832 , 81.51140594, 66.33999634, 130.59109497, 37.24206924),
       (35.08446503,  97.62433624, 43.83200836, 20.34561157, 128.53627014, 35.80251312, 41.59314346, 80.89860535, 66.15000153, 128.53627014, 35.08446503),
       (35.34244537,  97.63354492, 42.79874039, 19.90727234, 125.76422119, 36.07437897, 40.98268127, 80.28580475, 64.58000183, 125.76422119, 35.34244537),
       (36.25707626,  99.00255585, 42.57216263, 19.91554451, 124.94229126, 36.52467346, 41.50337982, 82.63342285, 65.52999878, 124.94229126, 36.25707626),
       (37.28897095, 102.80648041, 43.67792892, 20.15538216, 127.65791321, 36.966465  , 42.72432327, 84.13523865, 66.63999939, 127.65791321, 37.28897095)],
      dtype=[('AA', '<f8'), ('AAPL', '<f8'), ('DAL', '<f8'), ('GE', '<f8'), ('IBM', '<f8'), ('KO', '<f8'), ('MSFT', '<f8'), ('PEP', '<f8'), ('UAL', '<f8'), ('GALLY', '<f8'), ('FAF', '<f8')])

Concatenate Arrays by Row

concat_row = pp.concat(removed[["DAL","GE"]], added[["PEP","UAL"]], type="row"); concat_row[:5]
array([(44.57522202, 20.72605705), (43.83200836, 20.34561157),
       (42.79874039, 19.90727234), (42.57216263, 19.91554451),
       (43.67792892, 20.15538216)], dtype=[('DAL', '<f8'), ('GE', '<f8')])

Concatenate Arrays by Column

concat_col = pp.concat(removed[["DAL","GE"]], added[["PEP","UAL"]], type="columns"); concat_col[:5]
array([(44.57522202, 20.72605705, 81.51140594, 66.33999634),
       (43.83200836, 20.34561157, 80.89860535, 66.15000153),
       (42.79874039, 19.90727234, 80.28580475, 64.58000183),
       (42.57216263, 19.91554451, 82.63342285, 65.52999878),
       (43.67792892, 20.15538216, 84.13523865, 66.63999939)],
      dtype=[('DAL', '<f8'), ('GE', '<f8'), ('PEP', '<f8'), ('UAL', '<f8')])

Concatenate by Array

concat_array = pp.concat(removed[["DAL","GE"]], added[["PEP","UAL"]], type="array"); concat_array[:5]
array([[(44.57522201538086, 20.726057052612305),
        (43.832008361816406, 20.345611572265625),
        (42.79874038696289, 19.907272338867188), ...,
        (59.08000183105469, 11.180000305175781),
        (58.65999984741211, 11.079999923706055),
        (58.47999954223633, 11.15999984741211)],
       [(81.51140594482422, 66.33999633789062),
        (80.89860534667969, 66.1500015258789),
        (80.28580474853516, 64.58000183105469), ...,
        (137.5399932861328, 88.37000274658203),
        (136.80999755859375, 87.95999908447266),
        (136.6699981689453, 88.08999633789062)]], dtype=object)

Concatenate by Melt

concat_melt = pp.concat(removed[["DAL","GE"]], added[["PEP","UAL"]], type="melt"); concat_melt[:5]
array([(44.57522202, 20.72605705), (43.83200836, 20.34561157),
       (42.79874039, 19.90727234), (42.57216263, 19.91554451),
       (43.67792892, 20.15538216)], dtype=[('DAL', '<f8'), ('GE', '<f8')])

Merge Array (inner, outer)

merged = pp.merge(tsla_sub, crm_adj, left_on="Date", right_on="Date",how="inner",left_postscript="_TSLA",right_postscript="_CRM"); merged[:5]
array([('2019-01-02', 310.11999512, 135.55000305, 11658600),
       ('2019-01-03', 300.35998535, 130.3999939 ,  6965200),
       ('2019-01-04', 317.69000244, 137.96000671,  7394100),
       ('2019-01-07', 334.95999146, 142.22000122,  7551200),
       ('2019-01-08', 335.3500061 , 145.72000122,  7008500)],
      dtype=[('Date', '<M8[D]'), ('Adj_Close_TSLA', '<f8'), ('Adj_Close_CRM', '<f8'), ('Volume', '<i8')])

Replace Individual Values

## More work to done on replace (structured)
## replace(merged,original=317.69000244, replacement=np.nan)[:5]

Print Table

pp.table(merged[:5])
DateAdj_Close_TSLAAdj_Close_CRMVolume
02019-01-02310.120135.55011658600
12019-01-03300.360130.4006965200
22019-01-04317.690137.9607394100
32019-01-07334.960142.2207551200
42019-01-08335.350145.7207008500
### This is the new function that you should include above
### You can add the same peculuarities to remove

Add and Concatenate

tsla = pp.add(tsla,["Ticker"], "TSLA", "U10")
crm = pp.add(crm,["Ticker"], "CRM", "U10")
combine = pp.concat(tsla[0:5], crm[0:5], type="row"); combine
array([(315.13000488, 298.79998779, 306.1000061 , 310.11999512, 11658600, 310.11999512, '2019-01-02', 'TSLA'),
       (309.3999939 , 297.38000488, 307.        , 300.35998535,  6965200, 300.35998535, '2019-01-03', 'TSLA'),
       (318.        , 302.73001099, 306.        , 317.69000244,  7394100, 317.69000244, '2019-01-04', 'TSLA'),
       (336.73999023, 317.75      , 321.72000122, 334.95999146,  7551200, 334.95999146, '2019-01-07', 'TSLA'),
       (344.01000977, 327.01998901, 341.95999146, 335.3500061 ,  7008500, 335.3500061 , '2019-01-08', 'TSLA'),
       (136.83000183, 133.05000305, 133.3999939 , 135.55000305,  4783900, 135.55000305, '2019-01-02', 'CRM'),
       (134.77999878, 130.1000061 , 133.47999573, 130.3999939 ,  6365700, 130.3999939 , '2019-01-03', 'CRM'),
       (139.32000732, 132.22000122, 133.5       , 137.96000671,  6650600, 137.96000671, '2019-01-04', 'CRM'),
       (143.38999939, 138.78999329, 141.02000427, 142.22000122,  9064800, 142.22000122, '2019-01-07', 'CRM'),
       (146.46000671, 142.88999939, 144.72999573, 145.72000122,  9057300, 145.72000122, '2019-01-08', 'CRM')],
      dtype=[('High', '<f8'), ('Low', '<f8'), ('Open', '<f8'), ('Close', '<f8'), ('Volume', '<i8'), ('Adj_Close', '<f8'), ('Date', '<M8[D]'), ('Ticker', '<U10')])
dropped = pp.drop(combine,["High","Low","Open"]); dropped[:10]
array([(310.11999512, 11658600, 310.11999512, '2019-01-02', 'TSLA'),
       (300.35998535,  6965200, 300.35998535, '2019-01-03', 'TSLA'),
       (317.69000244,  7394100, 317.69000244, '2019-01-04', 'TSLA'),
       (334.95999146,  7551200, 334.95999146, '2019-01-07', 'TSLA'),
       (335.3500061 ,  7008500, 335.3500061 , '2019-01-08', 'TSLA'),
       (135.55000305,  4783900, 135.55000305, '2019-01-02', 'CRM'),
       (130.3999939 ,  6365700, 130.3999939 , '2019-01-03', 'CRM'),
       (137.96000671,  6650600, 137.96000671, '2019-01-04', 'CRM'),
       (142.22000122,  9064800, 142.22000122, '2019-01-07', 'CRM'),
       (145.72000122,  9057300, 145.72000122, '2019-01-08', 'CRM')],
      dtype={'names':['Close','Volume','Adj_Close','Date','Ticker'], 'formats':['<f8','<i8','<f8','<M8[D]','<U10'], 'offsets':[24,32,40,48,56], 'itemsize':96})

Pivot Array

piv = pp.pivot(dropped,"Date","Ticker","Adj_Close",display=True)
Adj_CloseCRMTSLA
2019-01-02135.55310.12
2019-01-03130.40300.36
2019-01-04137.96317.69
2019-01-07142.22334.96
2019-01-08145.72335.35

Add New Data types

tsla_extended = pp.add(tsla,"Month",tsla["Date"],'datetime64[M]')
tsla_extended = pp.add(tsla_extended,"Year",tsla_extended["Date"],'datetime64[Y]')

Update Existing Column

## faster method elsewhere
year_frame = pp.update(tsla,"Date", [dt.year for dt in tsla["Date"].astype(object)],types="|U10"); year_frame[:5]
array([(315.13000488, 298.79998779, 306.1000061 , 310.11999512, 11658600, 310.11999512, 'TSLA', '2019'),
       (309.3999939 , 297.38000488, 307.        , 300.35998535,  6965200, 300.35998535, 'TSLA', '2019'),
       (318.        , 302.73001099, 306.        , 317.69000244,  7394100, 317.69000244, 'TSLA', '2019'),
       (336.73999023, 317.75      , 321.72000122, 334.95999146,  7551200, 334.95999146, 'TSLA', '2019'),
       (344.01000977, 327.01998901, 341.95999146, 335.3500061 ,  7008500, 335.3500061 , 'TSLA', '2019')],
      dtype=[('High', '<f8'), ('Low', '<f8'), ('Open', '<f8'), ('Close', '<f8'), ('Volume', '<i8'), ('Adj_Close', '<f8'), ('Ticker', '<U10'), ('Date', '<U10')])

Group Arrays By

grouped = pp.group(tsla_extended, ['Ticker','Month','Year'],['mean', 'std', 'min', 'max'], ['Adj_Close','Close'], display=True)
TickerMonthYearAdj_Close_meanAdj_Close_stdAdj_Close_minAdj_Close_maxClose_meanClose_stdClose_minClose_max
0TSLA2019-01-012019-01-01318.49421.098287.590347.310318.49421.098287.590347.310
1TSLA2019-02-012019-01-01307.7288.053291.230321.350307.7288.053291.230321.350
2TSLA2019-03-012019-01-01277.7578.925260.420294.790277.7578.925260.420294.790
3TSLA2019-04-012019-01-01266.65614.985235.140291.810266.65614.985235.140291.810
4TSLA2019-05-012019-01-01219.71524.040185.160255.340219.71524.040185.160255.340
5TSLA2019-06-012019-01-01213.71712.125178.970226.430213.71712.125178.970226.430
6TSLA2019-07-012019-01-01242.38212.077224.550264.880242.38212.077224.550264.880
7TSLA2019-08-012019-01-01225.1037.831211.400238.300225.1037.831211.400238.300
8TSLA2019-09-012019-01-01237.2618.436220.680247.100237.2618.436220.680247.100
9TSLA2019-10-012019-01-01266.35531.463231.430328.130266.35531.463231.430328.130
10TSLA2019-11-012019-01-01338.30013.226313.310359.520338.30013.226313.310359.520
11TSLA2019-12-012019-01-01377.69536.183330.370430.940377.69536.183330.370430.940

Convert Array to Pandas

grouped_frame = pp.pandas(grouped); grouped_frame.head()
Ticker Month Year Adj_Close_mean Adj_Close_std Adj_Close_min Adj_Close_max Close_mean Close_std Close_min Close_max
0 TSLA 2019-01-01 2019-01-01 318.494284 21.098362 287.589996 347.309998 318.494284 21.098362 287.589996 347.309998
1 TSLA 2019-02-01 2019-01-01 307.728421 8.052522 291.230011 321.350006 307.728421 8.052522 291.230011 321.350006
2 TSLA 2019-03-01 2019-01-01 277.757140 8.924873 260.420013 294.790009 277.757140 8.924873 260.420013 294.790009
3 TSLA 2019-04-01 2019-01-01 266.655716 14.984572 235.139999 291.809998 266.655716 14.984572 235.139999 291.809998
4 TSLA 2019-05-01 2019-01-01 219.715454 24.039647 185.160004 255.339996 219.715454 24.039647 185.160004 255.339996

From Pandas to Structured

struct = pp.structured(grouped_frame); struct[:5]
rec.array([('TSLA', '2019-01-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000', 318.49428449, 21.09836186, 287.58999634, 347.30999756, 318.49428449, 21.09836186, 287.58999634, 347.30999756),
           ('TSLA', '2019-02-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000', 307.72842086,  8.05252198, 291.23001099, 321.3500061 , 307.72842086,  8.05252198, 291.23001099, 321.3500061 ),
           ('TSLA', '2019-03-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000', 277.75713966,  8.92487345, 260.42001343, 294.79000854, 277.75713966,  8.92487345, 260.42001343, 294.79000854),
           ('TSLA', '2019-04-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000', 266.65571594, 14.98457194, 235.13999939, 291.80999756, 266.65571594, 14.98457194, 235.13999939, 291.80999756),
           ('TSLA', '2019-05-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000', 219.7154541 , 24.03964724, 185.16000366, 255.33999634, 219.7154541 , 24.03964724, 185.16000366, 255.33999634)],
          dtype=[('Ticker', 'O'), ('Month', '<M8[ns]'), ('Year', '<M8[ns]'), ('Adj_Close_mean', '<f8'), ('Adj_Close_std', '<f8'), ('Adj_Close_min', '<f8'), ('Adj_Close_max', '<f8'), ('Close_mean', '<f8'), ('Close_std', '<f8'), ('Close_min', '<f8'), ('Close_max', '<f8')])

Shift Column

pp.shift(merged["Adj_Close_TSLA"],1)[:5]
array([         nan, 310.11999512, 300.35998535, 317.69000244,
       334.95999146])

Multiple Lags for Column

tsla_lagged = pp.lags(tsla_extended, "Adj_Close", 5); tsla_lagged[:5]
array([(315.13000488, 298.79998779, 306.1000061 , 310.11999512, 11658600, 310.11999512, '2019-01-02', 'TSLA', '2019-01', '2019',          nan,          nan,          nan,          nan, nan),
       (309.3999939 , 297.38000488, 307.        , 300.35998535,  6965200, 300.35998535, '2019-01-03', 'TSLA', '2019-01', '2019', 310.11999512,          nan,          nan,          nan, nan),
       (318.        , 302.73001099, 306.        , 317.69000244,  7394100, 317.69000244, '2019-01-04', 'TSLA', '2019-01', '2019', 300.35998535, 310.11999512,          nan,          nan, nan),
       (336.73999023, 317.75      , 321.72000122, 334.95999146,  7551200, 334.95999146, '2019-01-07', 'TSLA', '2019-01', '2019', 317.69000244, 300.35998535, 310.11999512,          nan, nan),
       (344.01000977, 327.01998901, 341.95999146, 335.3500061 ,  7008500, 335.3500061 , '2019-01-08', 'TSLA', '2019-01', '2019', 334.95999146, 317.69000244, 300.35998535, 310.11999512, nan)],
      dtype=[('High', '<f8'), ('Low', '<f8'), ('Open', '<f8'), ('Close', '<f8'), ('Volume', '<i8'), ('Adj_Close', '<f8'), ('Date', '<M8[D]'), ('Ticker', '<U10'), ('Month', '<M8[M]'), ('Year', '<M8[Y]'), ('Adj_Close_lag_1', '<f8'), ('Adj_Close_lag_2', '<f8'), ('Adj_Close_lag_3', '<f8'), ('Adj_Close_lag_4', '<f8'), ('Adj_Close_lag_5', '<f8')])

Correlation Array

correlated = pp.corr(closing)
CorrelationAAAAPLDALGEIBMKOMSFTPEPUAL
AA1.000.210.24-0.170.39-0.090.05-0.040.12
AAPL0.211.000.86-0.830.220.850.940.850.82
DAL0.240.861.00-0.780.140.790.860.780.86
GE-0.17-0.83-0.781.000.06-0.76-0.86-0.69-0.76
IBM0.390.220.140.061.000.070.150.240.18
KO-0.090.850.79-0.760.071.000.940.960.74
MSFT0.050.940.86-0.860.150.941.000.930.83
PEP-0.040.850.78-0.690.240.960.931.000.75
UAL0.120.820.86-0.760.180.740.830.751.00

Log Returns

pp.returns(closing,"IBM",type="log")
array([        nan, -0.01585991, -0.02180223, ...,  0.0026649 ,
       -0.0183533 ,  0.0092187 ])

Normal Returns

loga = pp.returns(closing,"IBM",type="normal"); loga
array([        nan, -0.0157348 , -0.02156628, ...,  0.00266845,
       -0.0181859 ,  0.00926132])

Add Column

close_ret = pp.add(closing,"IBM_log_return",loga); close_ret[:5]
array([(37.24206924, 100.45429993, 44.57522202, 20.72605705, 130.59109497, 35.80251312, 41.9791832 , 81.51140594, 66.33999634,         nan),
       (35.08446503,  97.62433624, 43.83200836, 20.34561157, 128.53627014, 35.80251312, 41.59314346, 80.89860535, 66.15000153, -0.0157348 ),
       (35.34244537,  97.63354492, 42.79874039, 19.90727234, 125.76422119, 36.07437897, 40.98268127, 80.28580475, 64.58000183, -0.02156628),
       (36.25707626,  99.00255585, 42.57216263, 19.91554451, 124.94229126, 36.52467346, 41.50337982, 82.63342285, 65.52999878, -0.00653548),
       (37.28897095, 102.80648041, 43.67792892, 20.15538216, 127.65791321, 36.966465  , 42.72432327, 84.13523865, 66.63999939,  0.02173501)],
      dtype=[('AA', '<f8'), ('AAPL', '<f8'), ('DAL', '<f8'), ('GE', '<f8'), ('IBM', '<f8'), ('KO', '<f8'), ('MSFT', '<f8'), ('PEP', '<f8'), ('UAL', '<f8'), ('IBM_log_return', '<f8')])

Drop Array Rows Where Null

close_ret_na = pp.dropnarow(close_ret, "IBM_log_return"); close_ret[:5]
array([(37.24206924, 100.45429993, 44.57522202, 20.72605705, 130.59109497, 35.80251312, 41.9791832 , 81.51140594, 66.33999634,         nan),
       (35.08446503,  97.62433624, 43.83200836, 20.34561157, 128.53627014, 35.80251312, 41.59314346, 80.89860535, 66.15000153, -0.0157348 ),
       (35.34244537,  97.63354492, 42.79874039, 19.90727234, 125.76422119, 36.07437897, 40.98268127, 80.28580475, 64.58000183, -0.02156628),
       (36.25707626,  99.00255585, 42.57216263, 19.91554451, 124.94229126, 36.52467346, 41.50337982, 82.63342285, 65.52999878, -0.00653548),
       (37.28897095, 102.80648041, 43.67792892, 20.15538216, 127.65791321, 36.966465  , 42.72432327, 84.13523865, 66.63999939,  0.02173501)],
      dtype=[('AA', '<f8'), ('AAPL', '<f8'), ('DAL', '<f8'), ('GE', '<f8'), ('IBM', '<f8'), ('KO', '<f8'), ('MSFT', '<f8'), ('PEP', '<f8'), ('UAL', '<f8'), ('IBM_log_return', '<f8')])

Portfolio Value from Log Return

pp.portfolio_value(close_ret_na, "IBM_log_return", type="log")
array([0.98438834, 0.96338604, 0.95711037, ..., 1.15115429, 1.13040872,
       1.14092643])

Cummulative Value from Log Return

pp.cummulative_return(close_ret_na, "IBM_log_return", type="log")
array([-0.01561166, -0.03661396, -0.04288963, ...,  0.15115429,
        0.13040872,  0.14092643])

Fillna Mean

pp.fillna(tsla_lagged,type="mean")[:5]
array([(315.13000488, 298.79998779, 306.1000061 , 310.11999512, 11658600, 310.11999512, 272.95330665, 272.38631982, 271.75180703, 271.10991915, 270.48587024),
       (309.3999939 , 297.38000488, 307.        , 300.35998535,  6965200, 300.35998535, 310.11999512, 272.38631982, 271.75180703, 271.10991915, 270.48587024),
       (318.        , 302.73001099, 306.        , 317.69000244,  7394100, 317.69000244, 300.35998535, 310.11999512, 271.75180703, 271.10991915, 270.48587024),
       (336.73999023, 317.75      , 321.72000122, 334.95999146,  7551200, 334.95999146, 317.69000244, 300.35998535, 310.11999512, 271.10991915, 270.48587024),
       (344.01000977, 327.01998901, 341.95999146, 335.3500061 ,  7008500, 335.3500061 , 334.95999146, 317.69000244, 300.35998535, 310.11999512, 270.48587024)],
      dtype={'names':['High','Low','Open','Close','Volume','Adj_Close','Adj_Close_lag_1','Adj_Close_lag_2','Adj_Close_lag_3','Adj_Close_lag_4','Adj_Close_lag_5'], 'formats':['<f8','<f8','<f8','<f8','<i8','<f8','<f8','<f8','<f8','<f8','<f8'], 'offsets':[0,8,16,24,32,40,112,120,128,136,144], 'itemsize':152})

Fillna Value

pp.fillna(tsla_lagged,type="value",value=-999999)[:5]
array([(315.13000488, 298.79998779, 306.1000061 , 310.11999512, 11658600, 310.11999512, -9.99999000e+05, -9.99999000e+05, -9.99999000e+05, -9.99999000e+05, -999999.),
       (309.3999939 , 297.38000488, 307.        , 300.35998535,  6965200, 300.35998535,  3.10119995e+02, -9.99999000e+05, -9.99999000e+05, -9.99999000e+05, -999999.),
       (318.        , 302.73001099, 306.        , 317.69000244,  7394100, 317.69000244,  3.00359985e+02,  3.10119995e+02, -9.99999000e+05, -9.99999000e+05, -999999.),
       (336.73999023, 317.75      , 321.72000122, 334.95999146,  7551200, 334.95999146,  3.17690002e+02,  3.00359985e+02,  3.10119995e+02, -9.99999000e+05, -999999.),
       (344.01000977, 327.01998901, 341.95999146, 335.3500061 ,  7008500, 335.3500061 ,  3.34959991e+02,  3.17690002e+02,  3.00359985e+02,  3.10119995e+02, -999999.)],
      dtype={'names':['High','Low','Open','Close','Volume','Adj_Close','Adj_Close_lag_1','Adj_Close_lag_2','Adj_Close_lag_3','Adj_Close_lag_4','Adj_Close_lag_5'], 'formats':['<f8','<f8','<f8','<f8','<i8','<f8','<f8','<f8','<f8','<f8','<f8'], 'offsets':[0,8,16,24,32,40,112,120,128,136,144], 'itemsize':152})

Fillna Forward Fill

pp.fillna(tsla_lagged,type="ffill")[:5]
array([(315.13000488, 298.79998779, 306.1000061 , 310.11999512, 11658600, 310.11999512,          nan,          nan,          nan,          nan, nan),
       (309.3999939 , 297.38000488, 307.        , 300.35998535,  6965200, 300.35998535, 310.11999512,          nan,          nan,          nan, nan),
       (318.        , 302.73001099, 306.        , 317.69000244,  7394100, 317.69000244, 300.35998535, 310.11999512,          nan,          nan, nan),
       (336.73999023, 317.75      , 321.72000122, 334.95999146,  7551200, 334.95999146, 317.69000244, 300.35998535, 310.11999512,          nan, nan),
       (344.01000977, 327.01998901, 341.95999146, 335.3500061 ,  7008500, 335.3500061 , 334.95999146, 317.69000244, 300.35998535, 310.11999512, nan)],
      dtype={'names':['High','Low','Open','Close','Volume','Adj_Close','Adj_Close_lag_1','Adj_Close_lag_2','Adj_Close_lag_3','Adj_Close_lag_4','Adj_Close_lag_5'], 'formats':['<f8','<f8','<f8','<f8','<i8','<f8','<f8','<f8','<f8','<f8','<f8'], 'offsets':[0,8,16,24,32,40,112,120,128,136,144], 'itemsize':152})

Fillna Backward Fill

pp.fillna(tsla_lagged,type="bfill")[:5]
array([(315.13000488, 298.79998779, 306.1000061 , 310.11999512, 11658600, 310.11999512, 310.11999512, 310.11999512, 310.11999512, 310.11999512, 310.11999512),
       (309.3999939 , 297.38000488, 307.        , 300.35998535,  6965200, 300.35998535, 310.11999512, 310.11999512, 310.11999512, 310.11999512, 310.11999512),
       (318.        , 302.73001099, 306.        , 317.69000244,  7394100, 317.69000244, 300.35998535, 310.11999512, 310.11999512, 310.11999512, 310.11999512),
       (336.73999023, 317.75      , 321.72000122, 334.95999146,  7551200, 334.95999146, 317.69000244, 300.35998535, 310.11999512, 310.11999512, 310.11999512),
       (344.01000977, 327.01998901, 341.95999146, 335.3500061 ,  7008500, 335.3500061 , 334.95999146, 317.69000244, 300.35998535, 310.11999512, 310.11999512)],
      dtype={'names':['High','Low','Open','Close','Volume','Adj_Close','Adj_Close_lag_1','Adj_Close_lag_2','Adj_Close_lag_3','Adj_Close_lag_4','Adj_Close_lag_5'], 'formats':['<f8','<f8','<f8','<f8','<i8','<f8','<f8','<f8','<f8','<f8','<f8'], 'offsets':[0,8,16,24,32,40,112,120,128,136,144], 'itemsize':152})

Print Table

pp.table(tsla_lagged,5)
HighLowOpenCloseVolumeAdj_CloseDateTickerMonthYearAdj_Close_lag_1Adj_Close_lag_2Adj_Close_lag_3Adj_Close_lag_4Adj_Close_lag_5
0315.130298.800306.100310.12011658600310.1202019-01-02TSLA2019-01-012019-01-01nannannannannan
1309.400297.380307.000300.3606965200300.3602019-01-03TSLA2019-01-012019-01-01310.120nannannannan
2318.000302.730306.000317.6907394100317.6902019-01-04TSLA2019-01-012019-01-01300.360310.120nannannan
3336.740317.750321.720334.9607551200334.9602019-01-07TSLA2019-01-012019-01-01317.690300.360310.120nannan
4344.010327.020341.960335.3507008500335.3502019-01-08TSLA2019-01-012019-01-01334.960317.690300.360310.120nan

Outliers

signal = tsla_lagged["Volume"]
z_signal = (signal - np.mean(signal)) / np.std(signal)
tsla_lagged = pp.add(tsla_lagged,"z_signal_volume",z_signal)
outliers = pp.detect(tsla_lagged["z_signal_volume"]); outliers
[12, 40, 42, 64, 78, 79, 84, 95, 97, 98, 107, 141, 205, 206, 207]
import matplotlib.pyplot as plt

plt.figure(figsize=(15, 7))
plt.plot(np.arange(len(tsla_lagged["Volume"])), tsla_lagged["Volume"])
plt.plot(np.arange(len(tsla_lagged["Volume"])), tsla_lagged["Volume"], 'X', label='outliers',markevery=outliers, c='r')
plt.legend()
plt.show()

png

Remove Noise

price_signal = tsla_lagged["Close"]
removed_signal = pp.removal(price_signal, 30)
noise = pp.get(price_signal, removed_signal)
plt.figure(figsize=(15, 7))
plt.subplot(2, 1, 1)
plt.plot(removed_signal)
plt.title('timeseries without noise')
plt.subplot(2, 1, 2)
plt.plot(noise)
plt.title('noise timeseries')
plt.show()

png

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