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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>')
/usr/share/miniconda3/envs/dask-examples/lib/python3.8/site-packages/IPython/core/display.py:717: 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 00:00:00,986,Yvonne,-0.46735166336132306,-0.5284651385903008
2000-01-01 00:00:01,1011,Dan,-0.6790377121970697,0.8700105617771643
2000-01-01 00:00:02,1013,Edith,0.418178614428417,-0.14669633816996352
2000-01-01 00:00:03,996,Victor,-0.5198557400468367,-0.8652698010069093
2000-01-01 00:00:04,996,Charlie,-0.9610545365394867,-0.9427385795794512
2000-01-01 00:00:05,990,Patricia,0.27569885239990777,-0.31787170745546645
2000-01-01 00:00:06,991,Dan,0.4957000956527089,-0.21546337885297828
2000-01-01 00:00:07,1015,Hannah,0.4916227762876131,0.8751971122211397
2000-01-01 00:00:08,983,Oliver,-0.8366077544280086,-0.21686128549035133
[7]:
!head data/2000-01-30.csv
timestamp,id,name,x,y
2000-01-30 00:00:00,987,Xavier,-0.9915377899142879,0.7908501574728211
2000-01-30 00:00:01,998,Patricia,-0.4263259190839772,0.11500038274372204
2000-01-30 00:00:02,965,Sarah,-0.6709731016475728,0.3869607902793153
2000-01-30 00:00:03,969,Wendy,-0.15867631498355084,0.4099494577986811
2000-01-30 00:00:04,964,Quinn,0.6233339948956649,-0.03215189901206794
2000-01-30 00:00:05,1058,Ingrid,-0.6432031748776097,0.7886701836849246
2000-01-30 00:00:06,1027,Victor,0.9508744730823087,0.8786747914899862
2000-01-30 00:00:07,975,Tim,0.6181908287513274,0.28870525524239965
2000-01-30 00:00:08,975,Edith,0.9633916421446942,0.7811130309205616

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 00:00:00 986 Yvonne -0.467352 -0.528465
1 2000-01-01 00:00:01 1011 Dan -0.679038 0.870011
2 2000-01-01 00:00:02 1013 Edith 0.418179 -0.146696
3 2000-01-01 00:00:03 996 Victor -0.519856 -0.865270
4 2000-01-01 00:00:04 996 Charlie -0.961055 -0.942739
[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: read-csv, 30 tasks
[10]:
df.head()
[10]:
timestamp id name x y
0 2000-01-01 00:00:00 986 Yvonne -0.467352 -0.528465
1 2000-01-01 00:00:01 1011 Dan -0.679038 0.870011
2 2000-01-01 00:00:02 1013 Edith 0.418179 -0.146696
3 2000-01-01 00:00:03 996 Victor -0.519856 -0.865270
4 2000-01-01 00:00:04 996 Charlie -0.961055 -0.942739

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: read-csv, 30 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 3.77 s, sys: 467 ms, total: 4.23 s
Wall time: 3.67 s
[14]:
name
Alice      -0.001538
Bob        -0.000560
Charlie    -0.001016
Dan         0.001308
Edith       0.000495
Frank      -0.002034
George     -0.003631
Hannah      0.000827
Ingrid      0.000508
Jerry      -0.006214
Kevin      -0.001283
Laura       0.001582
Michael     0.000969
Norbert     0.002641
Oliver     -0.000906
Patricia    0.000762
Quinn      -0.001863
Ray        -0.001239
Sarah      -0.001611
Tim         0.000619
Ursula     -0.001513
Victor     -0.000515
Wendy      -0.002153
Xavier     -0.000080
Yvonne     -0.000068
Zelda       0.001668
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.14.parquet  part.21.parquet  part.29.parquet
_metadata         part.15.parquet  part.22.parquet  part.3.parquet
part.0.parquet    part.16.parquet  part.23.parquet  part.4.parquet
part.1.parquet    part.17.parquet  part.24.parquet  part.5.parquet
part.10.parquet   part.18.parquet  part.25.parquet  part.6.parquet
part.11.parquet   part.19.parquet  part.26.parquet  part.7.parquet
part.12.parquet   part.2.parquet   part.27.parquet  part.8.parquet
part.13.parquet   part.20.parquet  part.28.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 1.13 s, sys: 102 ms, total: 1.23 s
Wall time: 1.13 s
[18]:
name
Alice      -0.001538
Bob        -0.000560
Charlie    -0.001016
Dan         0.001308
Edith       0.000495
Frank      -0.002034
George     -0.003631
Hannah      0.000827
Ingrid      0.000508
Jerry      -0.006214
Kevin      -0.001283
Laura       0.001582
Michael     0.000969
Norbert     0.002641
Oliver     -0.000906
Patricia    0.000762
Quinn      -0.001863
Ray        -0.001239
Sarah      -0.001611
Tim         0.000619
Ursula     -0.001513
Victor     -0.000515
Wendy      -0.002153
Xavier     -0.000080
Yvonne     -0.000068
Zelda       0.001668
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 1.09 s, sys: 74.1 ms, total: 1.16 s
Wall time: 1.11 s
[19]:
name
Alice      -0.001538
Bob        -0.000560
Charlie    -0.001016
Dan         0.001308
Edith       0.000495
Frank      -0.002034
George     -0.003631
Hannah      0.000827
Ingrid      0.000508
Jerry      -0.006214
Kevin      -0.001283
Laura       0.001582
Michael     0.000969
Norbert     0.002641
Oliver     -0.000906
Patricia    0.000762
Quinn      -0.001863
Ray        -0.001239
Sarah      -0.001611
Tim         0.000619
Ursula     -0.001513
Victor     -0.000515
Wendy      -0.002153
Xavier     -0.000080
Yvonne     -0.000068
Zelda       0.001668
Name: x, dtype: float64

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