You can run this notebook in a live session Binder or view it on Github.

DataFrames: Read and Write Data

Dask Dataframes can read and store data in many of the same formats as Pandas dataframes. In this example we read and write data with the popular CSV and Parquet formats, and discuss best practices when using these formats.

[1]:
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/0eEsIA0O1iE?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/IPython/core/display.py:689: UserWarning: Consider using IPython.display.IFrame instead
  warnings.warn("Consider using IPython.display.IFrame instead")
[1]:

Start Dask Client for Dashboard

Starting the Dask Client is optional. It will provide a dashboard which is useful to gain insight on the computation.

The link to the dashboard will become visible when you create the client below. We recommend having it open on one side of your screen while using your notebook on the other side. This can take some effort to arrange your windows, but seeing them both at the same is very useful when learning.

[2]:
from dask.distributed import Client
client = Client(n_workers=1, threads_per_worker=4, processes=False, memory_limit='2GB')
client
[2]:

Client

Cluster

  • Workers: 1
  • Cores: 4
  • Memory: 2.00 GB

Create artificial dataset

First we create an artificial dataset and write it to many CSV files.

You don’t need to understand this section, we’re just creating a dataset for the rest of the notebook.

[3]:
import dask
df = dask.datasets.timeseries()
df
[3]:
Dask DataFrame Structure:
id name x y
npartitions=30
2000-01-01 int64 object float64 float64
2000-01-02 ... ... ... ...
... ... ... ... ...
2000-01-30 ... ... ... ...
2000-01-31 ... ... ... ...
Dask Name: make-timeseries, 30 tasks
[4]:
import os
import datetime

if not os.path.exists('data'):
    os.mkdir('data')

def name(i):
    """ Provide date for filename given index

    Examples
    --------
    >>> name(0)
    '2000-01-01'
    >>> name(10)
    '2000-01-11'
    """
    return str(datetime.date(2000, 1, 1) + i * datetime.timedelta(days=1))

df.to_csv('data/*.csv', name_function=name);

Read CSV files

We now have many CSV files in our data directory, one for each day in the month of January 2000. Each CSV file holds timeseries data for that day. We can read all of them as one logical dataframe using the dd.read_csv function with a glob string.

[5]:
!ls data/*.csv | head
data/2000-01-01.csv
data/2000-01-02.csv
data/2000-01-03.csv
data/2000-01-04.csv
data/2000-01-05.csv
data/2000-01-06.csv
data/2000-01-07.csv
data/2000-01-08.csv
data/2000-01-09.csv
data/2000-01-10.csv
[6]:
!head data/2000-01-01.csv
timestamp,id,name,x,y
2000-01-01 00:00:00,952,Frank,-0.44224662137183923,0.1265341125044399
2000-01-01 00:00:01,1031,Oliver,0.5831934756729826,-0.5444731023721892
2000-01-01 00:00:02,964,Alice,0.6172186151350065,-0.6781916460105943
2000-01-01 00:00:03,1045,Xavier,0.8316161815637875,-0.14306235487038887
2000-01-01 00:00:04,1025,Jerry,0.17459385470064337,0.28563352870159564
2000-01-01 00:00:05,1011,Zelda,0.30319517175783606,0.31515163554848336
2000-01-01 00:00:06,1019,Frank,-0.275115389967568,0.28472956110294056
2000-01-01 00:00:07,1048,Victor,0.9103823060260117,0.2536112012015619
2000-01-01 00:00:08,1016,Oliver,-0.9806169090211048,-0.8657131124639472
[7]:
!head data/2000-01-30.csv
timestamp,id,name,x,y
2000-01-30 00:00:00,979,Ray,0.54492228000167,0.9544755958047952
2000-01-30 00:00:01,1003,Patricia,0.5559803926535198,0.04257698409797528
2000-01-30 00:00:02,974,Ursula,-0.6686644038693852,-0.8008141574744356
2000-01-30 00:00:03,1002,George,-0.10483977451125481,-0.604858374188769
2000-01-30 00:00:04,1017,George,-0.8345199003400166,0.8334257071817961
2000-01-30 00:00:05,988,Dan,-0.41633171379490985,-0.9314850986419356
2000-01-30 00:00:06,972,Yvonne,-0.4480945423989644,-0.11499924738008516
2000-01-30 00:00:07,993,Zelda,0.8534777663901258,0.9529146971087892
2000-01-30 00:00:08,1015,Ray,-0.5546773931548821,-0.5052067141172891

We can read one file with pandas.read_csv or many files with dask.dataframe.read_csv

[8]:
import pandas as pd

df = pd.read_csv('data/2000-01-01.csv')
df.head()
[8]:
timestamp id name x y
0 2000-01-01 00:00:00 952 Frank -0.442247 0.126534
1 2000-01-01 00:00:01 1031 Oliver 0.583193 -0.544473
2 2000-01-01 00:00:02 964 Alice 0.617219 -0.678192
3 2000-01-01 00:00:03 1045 Xavier 0.831616 -0.143062
4 2000-01-01 00:00:04 1025 Jerry 0.174594 0.285634
[9]:
import dask.dataframe as dd

df = dd.read_csv('data/2000-*-*.csv')
df
[9]:
Dask DataFrame Structure:
timestamp id name x y
npartitions=30
object int64 object float64 float64
... ... ... ... ...
... ... ... ... ... ...
... ... ... ... ...
... ... ... ... ...
Dask Name: from-delayed, 90 tasks
[10]:
df.head()
[10]:
timestamp id name x y
0 2000-01-01 00:00:00 952 Frank -0.442247 0.126534
1 2000-01-01 00:00:01 1031 Oliver 0.583193 -0.544473
2 2000-01-01 00:00:02 964 Alice 0.617219 -0.678192
3 2000-01-01 00:00:03 1045 Xavier 0.831616 -0.143062
4 2000-01-01 00:00:04 1025 Jerry 0.174594 0.285634

Tuning read_csv

The Pandas read_csv function has many options to help you parse files. The Dask version uses the Pandas function internally, and so supports many of the same options. You can use the ? operator to see the full documentation string.

[11]:
pd.read_csv?
[12]:
dd.read_csv?

In this case we use the parse_dates keyword to parse the timestamp column to be a datetime. This will make things more efficient in the future. Notice that the dtype of the timestamp column has changed from object to datetime64[ns].

[13]:
df = dd.read_csv('data/2000-*-*.csv', parse_dates=['timestamp'])
df
[13]:
Dask DataFrame Structure:
timestamp id name x y
npartitions=30
datetime64[ns] int64 object float64 float64
... ... ... ... ...
... ... ... ... ... ...
... ... ... ... ...
... ... ... ... ...
Dask Name: from-delayed, 90 tasks

Do a simple computation

Whenever we operate on our dataframe we read through all of our CSV data so that we don’t fill up RAM. This is very efficient for memory use, but reading through all of the CSV files every time can be slow.

[14]:
%time df.groupby('name').x.mean().compute()
CPU times: user 7.08 s, sys: 448 ms, total: 7.52 s
Wall time: 5.12 s
[14]:
name
Alice       0.000947
Bob         0.000342
Charlie     0.000012
Dan         0.003754
Edith      -0.002709
Frank      -0.001991
George      0.001272
Hannah     -0.000637
Ingrid      0.000443
Jerry      -0.000371
Kevin       0.000635
Laura       0.000137
Michael    -0.001481
Norbert     0.001331
Oliver      0.002043
Patricia    0.003078
Quinn      -0.001270
Ray         0.001101
Sarah      -0.000470
Tim        -0.002478
Ursula     -0.001894
Victor      0.001413
Wendy       0.000305
Xavier      0.000349
Yvonne      0.000617
Zelda       0.001609
Name: x, dtype: float64
[ ]:

Write to Parquet

Instead, we’ll store our data in Parquet, a format that is more efficient for computers to read and write.

[15]:
df.to_parquet('data/2000-01.parquet', engine='pyarrow')
[16]:
!ls data/2000-01.parquet/
_common_metadata  part.16.parquet  part.23.parquet  part.3.parquet
part.0.parquet    part.17.parquet  part.24.parquet  part.4.parquet
part.10.parquet   part.18.parquet  part.25.parquet  part.5.parquet
part.11.parquet   part.19.parquet  part.26.parquet  part.6.parquet
part.12.parquet   part.1.parquet   part.27.parquet  part.7.parquet
part.13.parquet   part.20.parquet  part.28.parquet  part.8.parquet
part.14.parquet   part.21.parquet  part.29.parquet  part.9.parquet
part.15.parquet   part.22.parquet  part.2.parquet

Read from Parquet

[17]:
df = dd.read_parquet('data/2000-01.parquet', engine='pyarrow')
df
[17]:
Dask DataFrame Structure:
timestamp id name x y
npartitions=30
datetime64[ns] int64 object float64 float64
... ... ... ... ...
... ... ... ... ... ...
... ... ... ... ...
... ... ... ... ...
Dask Name: read-parquet, 30 tasks
[18]:
%time df.groupby('name').x.mean().compute()
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
CPU times: user 1.77 s, sys: 208 ms, total: 1.98 s
Wall time: 1.44 s
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
[18]:
name
Alice       0.000947
Bob         0.000342
Charlie     0.000012
Dan         0.003754
Edith      -0.002709
Frank      -0.001991
George      0.001272
Hannah     -0.000637
Ingrid      0.000443
Jerry      -0.000371
Kevin       0.000635
Laura       0.000137
Michael    -0.001481
Norbert     0.001331
Oliver      0.002043
Patricia    0.003078
Quinn      -0.001270
Ray         0.001101
Sarah      -0.000470
Tim        -0.002478
Ursula     -0.001894
Victor      0.001413
Wendy       0.000305
Xavier      0.000349
Yvonne      0.000617
Zelda       0.001609
Name: x, dtype: float64

Select only the columns that you plan to use

Parquet is a column-store, which means that it can efficiently pull out only a few columns from your dataset. This is good because it helps to avoid unnecessary data loading.

[19]:
%%time
df = dd.read_parquet('data/2000-01.parquet', columns=['name', 'x'], engine='pyarrow')
df.groupby('name').x.mean().compute()
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels
CPU times: user 1.47 s, sys: 168 ms, total: 1.64 s
Wall time: 1.2 s
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:707: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:734: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
/home/travis/miniconda/envs/test/lib/python3.6/site-packages/pyarrow/pandas_compat.py:751: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels

Here the difference is not that large, but with larger datasets this can save a great deal of time.