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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:701: 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 00:00:00,984,Tim,0.5628552119940264,-0.8954708421012165
2000-01-01 00:00:01,1028,Frank,0.19590086017663966,-0.37999986794495944
2000-01-01 00:00:02,991,Hannah,-0.5239914745034737,-0.5744402370352726
2000-01-01 00:00:03,993,Xavier,0.048311650570585174,0.6229332278241841
2000-01-01 00:00:04,997,Ingrid,-0.5079169113806308,0.42778152877398923
2000-01-01 00:00:05,1032,Victor,0.4812307347945848,-0.829643532910755
2000-01-01 00:00:06,972,George,-0.9062646306927706,0.20071257476953686
2000-01-01 00:00:07,1031,George,-0.17379265947511202,-0.18761863606595108
2000-01-01 00:00:08,975,Dan,0.9051469536435892,0.2829555200602185
[7]:
!head data/2000-01-30.csv
timestamp,id,name,x,y
2000-01-30 00:00:00,988,Dan,0.6509964014077678,-0.6343116877378341
2000-01-30 00:00:01,989,Quinn,-0.07663109520026756,-0.8274518293489848
2000-01-30 00:00:02,979,Bob,-0.7520926462773139,0.7148393704984126
2000-01-30 00:00:03,984,Dan,0.5290394175116311,-0.7814081974283549
2000-01-30 00:00:04,966,Kevin,-0.734354150267813,-0.5855340174515484
2000-01-30 00:00:05,1005,Quinn,0.9573060299892373,0.6920266189038038
2000-01-30 00:00:06,995,Patricia,0.8875964054785555,-0.85172465886282
2000-01-30 00:00:07,1020,Ingrid,-0.895411162853192,0.9660794917052853
2000-01-30 00:00:08,1009,Laura,0.23681405357813823,0.6487256403642159

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 00:00:00 984 Tim 0.562855 -0.895471
1 2000-01-01 00:00:01 1028 Frank 0.195901 -0.380000
2 2000-01-01 00:00:02 991 Hannah -0.523991 -0.574440
3 2000-01-01 00:00:03 993 Xavier 0.048312 0.622933
4 2000-01-01 00:00:04 997 Ingrid -0.507917 0.427782
[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 00:00:00 984 Tim 0.562855 -0.895471
1 2000-01-01 00:00:01 1028 Frank 0.195901 -0.380000
2 2000-01-01 00:00:02 991 Hannah -0.523991 -0.574440
3 2000-01-01 00:00:03 993 Xavier 0.048312 0.622933
4 2000-01-01 00:00:04 997 Ingrid -0.507917 0.427782

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 4.69 s, sys: 536 ms, total: 5.23 s
Wall time: 3.42 s
[14]:
name
Alice      -0.001754
Bob        -0.002218
Charlie    -0.000052
Dan         0.001172
Edith       0.000410
Frank      -0.000029
George     -0.001307
Hannah      0.003512
Ingrid     -0.003541
Jerry       0.002283
Kevin       0.000771
Laura      -0.000323
Michael     0.000082
Norbert     0.000097
Oliver      0.001725
Patricia    0.002653
Quinn       0.000191
Ray        -0.001908
Sarah       0.001738
Tim         0.002714
Ursula     -0.000715
Victor     -0.003372
Wendy      -0.001569
Xavier      0.001505
Yvonne     -0.001881
Zelda       0.000322
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.15.parquet  part.22.parquet  part.2.parquet
_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

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 1.46 s, sys: 102 ms, total: 1.56 s
Wall time: 1.04 s
[18]:
name
Alice      -0.001754
Bob        -0.002218
Charlie    -0.000052
Dan         0.001172
Edith       0.000410
Frank      -0.000029
George     -0.001307
Hannah      0.003512
Ingrid     -0.003541
Jerry       0.002283
Kevin       0.000771
Laura      -0.000323
Michael     0.000082
Norbert     0.000097
Oliver      0.001725
Patricia    0.002653
Quinn       0.000191
Ray        -0.001908
Sarah       0.001738
Tim         0.002714
Ursula     -0.000715
Victor     -0.003372
Wendy      -0.001569
Xavier      0.001505
Yvonne     -0.001881
Zelda       0.000322
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 1.28 s, sys: 75.1 ms, total: 1.36 s
Wall time: 935 ms
[19]:
name
Alice      -0.001754
Bob        -0.002218
Charlie    -0.000052
Dan         0.001172
Edith       0.000410
Frank      -0.000029
George     -0.001307
Hannah      0.003512
Ingrid     -0.003541
Jerry       0.002283
Kevin       0.000771
Laura      -0.000323
Michael     0.000082
Norbert     0.000097
Oliver      0.001725
Patricia    0.002653
Quinn       0.000191
Ray        -0.001908
Sarah       0.001738
Tim         0.002714
Ursula     -0.000715
Victor     -0.003372
Wendy      -0.001569
Xavier      0.001505
Yvonne     -0.001881
Zelda       0.000322
Name: x, dtype: float64

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