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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:694: 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 00:00:00,1007,Yvonne,-0.9980270955499588,-0.9536544991476237
2000-01-01 00:00:01,974,Oliver,-0.8274104683150951,0.07465451447949167
2000-01-01 00:00:02,967,Zelda,0.15615446890936502,0.6956374296643635
2000-01-01 00:00:03,984,Oliver,-0.9244724410118943,-0.5736637833462024
2000-01-01 00:00:04,987,Frank,0.058926578268793106,0.20701166193521603
2000-01-01 00:00:05,965,Sarah,0.3560867439558746,-0.5360514837062944
2000-01-01 00:00:06,1038,Victor,0.4334513040309058,-0.09911056948838137
2000-01-01 00:00:07,973,Wendy,0.19501772277382412,-0.8049288730344373
2000-01-01 00:00:08,1004,Bob,-0.06747262107938656,-0.1359110490139246
[7]:
!head data/2000-01-30.csv
timestamp,id,name,x,y
2000-01-30 00:00:00,1018,Oliver,0.20558928772125507,0.9776748254521574
2000-01-30 00:00:01,1039,Ingrid,0.7178924520822265,0.15291501794199225
2000-01-30 00:00:02,1021,Laura,0.9240902578781116,0.4712252486878603
2000-01-30 00:00:03,1003,George,0.7907889821417022,-0.42373767942415985
2000-01-30 00:00:04,1020,Wendy,0.4158680309203153,0.9913337248581864
2000-01-30 00:00:05,1012,Tim,-0.5252972856812725,0.04971122084200652
2000-01-30 00:00:06,1019,Michael,0.29852594959881373,-0.3579456787942523
2000-01-30 00:00:07,950,Hannah,0.4305168590852506,-0.9870258647373524
2000-01-30 00:00:08,972,Sarah,-0.6468744623195866,-0.46834792121826796

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 1007 Yvonne -0.998027 -0.953654
1 2000-01-01 00:00:01 974 Oliver -0.827410 0.074655
2 2000-01-01 00:00:02 967 Zelda 0.156154 0.695637
3 2000-01-01 00:00:03 984 Oliver -0.924472 -0.573664
4 2000-01-01 00:00:04 987 Frank 0.058927 0.207012
[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 1007 Yvonne -0.998027 -0.953654
1 2000-01-01 00:00:01 974 Oliver -0.827410 0.074655
2 2000-01-01 00:00:02 967 Zelda 0.156154 0.695637
3 2000-01-01 00:00:03 984 Oliver -0.924472 -0.573664
4 2000-01-01 00:00:04 987 Frank 0.058927 0.207012

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 5.52 s, sys: 693 ms, total: 6.21 s
Wall time: 4.2 s
[14]:
name
Alice      -0.000483
Bob         0.000492
Charlie    -0.002510
Dan         0.000785
Edith       0.000638
Frank      -0.000700
George      0.002844
Hannah     -0.001709
Ingrid     -0.000681
Jerry       0.000420
Kevin      -0.000732
Laura       0.005687
Michael     0.000053
Norbert     0.002767
Oliver     -0.001166
Patricia    0.001469
Quinn       0.001917
Ray         0.000276
Sarah       0.002105
Tim        -0.002295
Ursula      0.001870
Victor     -0.003921
Wendy      -0.002271
Xavier     -0.002804
Yvonne      0.003848
Zelda      -0.000809
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 1.87 s, sys: 123 ms, total: 1.99 s
Wall time: 1.42 s
[18]:
name
Alice      -0.000483
Bob         0.000492
Charlie    -0.002510
Dan         0.000785
Edith       0.000638
Frank      -0.000700
George      0.002844
Hannah     -0.001709
Ingrid     -0.000681
Jerry       0.000420
Kevin      -0.000732
Laura       0.005687
Michael     0.000053
Norbert     0.002767
Oliver     -0.001166
Patricia    0.001469
Quinn       0.001917
Ray         0.000276
Sarah       0.002105
Tim        -0.002295
Ursula      0.001870
Victor     -0.003921
Wendy      -0.002271
Xavier     -0.002804
Yvonne      0.003848
Zelda      -0.000809
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 1.71 s, sys: 47 ms, total: 1.76 s
Wall time: 1.29 s
[19]:
name
Alice      -0.000483
Bob         0.000492
Charlie    -0.002510
Dan         0.000785
Edith       0.000638
Frank      -0.000700
George      0.002844
Hannah     -0.001709
Ingrid     -0.000681
Jerry       0.000420
Kevin      -0.000732
Laura       0.005687
Michael     0.000053
Norbert     0.002767
Oliver     -0.001166
Patricia    0.001469
Quinn       0.001917
Ray         0.000276
Sarah       0.002105
Tim        -0.002295
Ursula      0.001870
Victor     -0.003921
Wendy      -0.002271
Xavier     -0.002804
Yvonne      0.003848
Zelda      -0.000809
Name: x, dtype: float64

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