The PyData Ecosystem

wish help you to fix your issue Note: The example below requires the latest version of blaze, which you can get via

conda install -c blaze blaze

pip install git+git://github.com/ContinuumIO/into.git

In [28]: from blaze import Data, compute

In [29]: ls -d *.bcolz
all.bcolz/  fare.bcolz/ trip.bcolz/

In [30]: d = Data('all.bcolz')

In [31]: d.head(5)
Out[31]:
                          medallion                      hack_license  \
0  89D227B655E5C82AECF13C3F540D4CF4  BA96DE419E711691B9445D6A6307C170
1  0BD7C8F5BA12B88E0B67BED28BEA73D8  9FD8F69F0804BDB5549F40E9DA1BE472
2  0BD7C8F5BA12B88E0B67BED28BEA73D8  9FD8F69F0804BDB5549F40E9DA1BE472
3  DFD2202EE08F7A8DC9A57B02ACB81FE2  51EE87E3205C985EF8431D850C786310
4  DFD2202EE08F7A8DC9A57B02ACB81FE2  51EE87E3205C985EF8431D850C786310

  vendor_id  rate_code store_and_fwd_flag     pickup_datetime  \
0       CMT          1                  N 2013-01-01 15:11:48
1       CMT          1                  N 2013-01-06 00:18:35
2       CMT          1                  N 2013-01-05 18:49:41
3       CMT          1                  N 2013-01-07 23:54:15
4       CMT          1                  N 2013-01-07 23:25:03

     dropoff_datetime  passenger_count  trip_time_in_secs  trip_distance  \
0 2013-01-01 15:18:10                4                382            1.0
1 2013-01-06 00:22:54                1                259            1.5
2 2013-01-05 18:54:23                1                282            1.1
3 2013-01-07 23:58:20                2                244            0.7
4 2013-01-07 23:34:24                1                560            2.1

     ...     pickup_latitude  dropoff_longitude  dropoff_latitude  \
0    ...           40.757977         -73.989838         40.751171
1    ...           40.731781         -73.994499         40.750660
2    ...           40.737770         -74.009834         40.726002
3    ...           40.759945         -73.984734         40.759388
4    ...           40.748528         -74.002586         40.747868

   tolls_amount  tip_amount  total_amount  mta_tax  fare_amount  payment_type  \
0             0           0           7.0      0.5          6.5           CSH
1             0           0           7.0      0.5          6.0           CSH
2             0           0           7.0      0.5          5.5           CSH
3             0           0           6.0      0.5          5.0           CSH
4             0           0          10.5      0.5          9.5           CSH

  surcharge
0       0.0
1       0.5
2       1.0
3       0.5
4       0.5

[5 rows x 21 columns]

In [32]: from multiprocessing import Pool

In [33]: p = Pool()

In [34]: %timeit -n 1 -r 1 values = compute(trip.medallion.distinct())
1 loops, best of 1: 1min per loop

In [35]: %timeit -n 1 -r 1 values = compute(trip.medallion.distinct(), map=p.map)
1 loops, best of 1: 16.2 s per loop

In [38]: %timeit -n 1 -r 1 values = compute(trip.passenger_count.distinct())
1 loops, best of 1: 3.33 s per loop

In [39]: %timeit -n 1 -r 1 values = compute(trip.passenger_count.distinct(), map=p.map)
1 loops, best of 1: 1.01 s per loop

around this issue I have a dataframe that has a few columns with floats and a few columns that are string. All columns have nan. The string columns have either strings or nan which appear to have a type float. When I try to 'df.to_hdf' to store the dataframe, I get the following error: , You can fill each column with the appropriate missing value. E.g.

import pandas as pd
import numpy as np

col1 = [1.0, np.nan, 3.0]
col2 = ['one', np.nan, 'three']

df = pd.DataFrame(dict(col1=col1, col2=col2))
df['col1'] = df['col1'].fillna(0.0)
df['col2'] = df['col2'].fillna('')

df.to_hdf('eg.hdf', 'eg')

Does that help The most used JS stack is the MEAN stack: MongoDB, Express, Angular, and Node. You can find two popular frameworks here:
mean.io meanjs.org

it fixes the issue I can give some part of the picture, although others were more involved. Blaze was both an umbrella project for incubating data-engineering ideas into released oss packages, and a package itself focussing on symbolic manipulations of data-frames and translating these into various backend execution engines, particularly database services. Critically, Blaze wanted to be the (start of a) solution for a very broad range of problems! In particular, the translation layer became very large and hard to maintain and by trying to cater to all, limited the range of operations that the symbolic layer could offer.
In terms of an umbrella project, Blaze was a success. Many ideas that started in Blaze percolated into the ecosystem. Probably the most prominent single project to come out of Blaze is Dask, which, while originally planned as an execution layer for Blaze, implements an even larger API of data-frame operations, as well as other high-level collections and arbitrary graph manipulation. Even fully symbolic optimisations exist in Dask, though this is perhaps not as complete. Other Anaconda-stable projects such as numba and bokeh were influenced by the Blaze effort, but I'll not talk about them here.

wish help you to fix your issue In a jupyter notebook you can execute shell commands by adding '!' infront of the command. For example, you can run:
! rasa train

from IPython.display import IFrame
IFrame("http://localhost:8888/terminals/2", width=1000, height=500)