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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/python3.7/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 00:00:00,1057,Hannah,-0.9507674565574422,0.22102886882244066
2000-01-01 00:00:01,973,Victor,-0.43021421722793884,0.37983237852784746
2000-01-01 00:00:02,1014,Alice,-0.028936373748150723,-0.7765888895158513
2000-01-01 00:00:03,983,Ursula,-0.4910589145137245,0.13207596591025927
2000-01-01 00:00:04,999,Victor,0.8561512699997151,0.26981732782483436
2000-01-01 00:00:05,1023,Frank,-0.793593328198217,-0.9980832163437914
2000-01-01 00:00:06,1004,Hannah,-0.9268269810863663,-0.020710792818779522
2000-01-01 00:00:07,987,Xavier,-0.997536642256786,-0.6395531371200232
2000-01-01 00:00:08,1005,Zelda,-0.6063540211635976,0.5372071619092507
[7]:
!head data/2000-01-30.csv
timestamp,id,name,x,y
2000-01-30 00:00:00,1006,Ingrid,0.21326698379801412,0.3812500314099361
2000-01-30 00:00:01,978,Victor,-0.6647735005955167,0.8689356683802794
2000-01-30 00:00:02,1016,Hannah,0.4891779031474157,0.15873857641705924
2000-01-30 00:00:03,1008,Kevin,-0.8683467722766003,-0.9454801405541802
2000-01-30 00:00:04,948,Alice,-0.4869943050621204,-0.8921761457963904
2000-01-30 00:00:05,1016,Sarah,0.17331398393995578,0.23913567041099149
2000-01-30 00:00:06,1008,Sarah,0.8186659719312175,0.9401651180188291
2000-01-30 00:00:07,994,Bob,-0.43980855399688235,-0.7481467945599216
2000-01-30 00:00:08,998,Yvonne,-0.033426548828043856,-0.17354509219707448

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 1057 Hannah -0.950767 0.221029
1 2000-01-01 00:00:01 973 Victor -0.430214 0.379832
2 2000-01-01 00:00:02 1014 Alice -0.028936 -0.776589
3 2000-01-01 00:00:03 983 Ursula -0.491059 0.132076
4 2000-01-01 00:00:04 999 Victor 0.856151 0.269817
[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 1057 Hannah -0.950767 0.221029
1 2000-01-01 00:00:01 973 Victor -0.430214 0.379832
2 2000-01-01 00:00:02 1014 Alice -0.028936 -0.776589
3 2000-01-01 00:00:03 983 Ursula -0.491059 0.132076
4 2000-01-01 00:00:04 999 Victor 0.856151 0.269817

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 6.76 s, sys: 468 ms, total: 7.23 s
Wall time: 4.99 s
[14]:
name
Alice       0.000770
Bob        -0.003358
Charlie     0.000719
Dan         0.000444
Edith      -0.003353
Frank       0.001502
George     -0.002829
Hannah     -0.001229
Ingrid     -0.000059
Jerry       0.000058
Kevin      -0.000236
Laura      -0.002906
Michael     0.001251
Norbert     0.003110
Oliver     -0.000477
Patricia    0.000149
Quinn      -0.000483
Ray        -0.000105
Sarah      -0.000952
Tim         0.002162
Ursula      0.001019
Victor     -0.002997
Wendy       0.001509
Xavier     -0.002275
Yvonne      0.001745
Zelda      -0.000564
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()
CPU times: user 1.65 s, sys: 288 ms, total: 1.94 s
Wall time: 1.34 s
[18]:
name
Alice       0.000770
Bob        -0.003358
Charlie     0.000719
Dan         0.000444
Edith      -0.003353
Frank       0.001502
George     -0.002829
Hannah     -0.001229
Ingrid     -0.000059
Jerry       0.000058
Kevin      -0.000236
Laura      -0.002906
Michael     0.001251
Norbert     0.003110
Oliver     -0.000477
Patricia    0.000149
Quinn      -0.000483
Ray        -0.000105
Sarah      -0.000952
Tim         0.002162
Ursula      0.001019
Victor     -0.002997
Wendy       0.001509
Xavier     -0.002275
Yvonne      0.001745
Zelda      -0.000564
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()
CPU times: user 1.39 s, sys: 124 ms, total: 1.51 s
Wall time: 1.09 s

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