Dask DataFrame

In this tutorial, you learn:

• Basic concepts and features of Dask DataFrames

• Applications of Dask DataFrames

• Interacting with Dask DataFrames

• Built-in operations with Dask DataFrames

• Dask DataFrames Best Practices

Related Documentation

• Dask DataFrame documentation

• Dask DataFrame API

• Dask DataFrame examples

• pandas documentation

Prerequisites

Concepts	Importance	Notes
Familiarity with Pandas DataFrame	Necessary
Dask Overview	Necessary

• Time to learn: 40 minutes

---

Introduction

Image credit: Dask Contributors

pandas is a very popular tool for working with tabular datasets, but the dataset needs to fit into the memory.

pandas operates best with smaller datasets, and if you have a large dataset, you’ll receive an out of memory error using pandas. A general rule of thumb for pandas is:

“Have 5 to 10 times as much RAM as the size of your dataset”

Wes McKinney (2017) in 10 things I hate about pandas

But Dask DataFrame can be used to solve pandas performance issues with larger-than-memory datasets.

What is Dask DataFrame?

• A Dask DataFrame is a parallel DataFrame composed of smaller pandas DataFrames (also known as partitions).

• Dask Dataframes look and feel like the pandas DataFrames on the surface.

• Dask DataFrames partition the data into manageable partitions that can be processed in parallel and across multiple cores or computers.

• Similar to Dask Arrays, Dask DataFrames are lazy!

  Unlike pandas, operations on Dask DataFrames are not computed until you explicitly request them (e.g. by calling .compute).

When to use Dask DataFrame and when to avoid it?

Dask DataFrames are used in situations where pandas fails or has poor performance due to data size.

Dask DataFrame is a good choice when doing parallalizeable computations.
Some examples are:

• Element-wise operations such as df.x + df.y

• Row-wise filtering such as df[df.x>0]

• Common aggregations such as df.x.max()

• Dropping duplicates such as df.x.drop_duplicate()

However, Dask is not great for operations that requires shuffling or re-indexing.
Some examples are:

• Set index: df.set_index(df.x)

WARNING: Although, Dask DataFrame has a very similar interface to the pandas DataFrame (as we will see in this tutorial), it does NOT include some of the pandas interface yet.

See the Dask DataFrame API documentation for a compehnsive list of available functions.

---

Tutorial Dataset

In this tutorial, we are going to use the NOAA Global Historical Climatology Network Daily (GHCN-D) dataset.
GHCN-D is a public available dataset that includes daily climate records from +100,000 surface observations around the world.
This is an example of a real dataset that is used by NCAR scientists for their research. GHCN-D raw dataset for all stations is available through NOAA Climate Data Online.

To learn more about GHCNd dataset, please visit:

• GHCNd Journal Paper

• GHCNd Official Website

Download the data

For this example, we are going to look through a subset of data from the GHCN-D dataset.

First, we look at the daily observations from Denver International Airport, next we are going to look through selected stations in the US.

The access the preprocessed dataset for this tutorial, please run the following script:

!./get_data.sh

Downloading https://docs.google.com/uc?export=download&id=14doSRn8hT14QYtjZz28GKv14JgdIsbFF

USC00023160.csv

USC00027281.csv
USC00027390.csv

USC00030936.csv
USC00031596.csv

USC00032444.csv

USC00035186.csv
USC00035754.csv

USC00035820.csv

USC00035908.csv
USC00042294.csv

USC00044259.csv

USC00048758.csv
USC00050848.csv

USC00051294.csv

USC00051528.csv
USC00051564.csv

USC00051741.csv
USC00052184.csv

USC00052281.csv
USC00052446.csv

USC00053005.csv

USC00053038.csv
USC00053146.csv

USC00053662.csv

USC00053951.csv
USC00054076.csv

USC00054770.csv
USC00054834.csv

USC00055322.csv
USC00055722.csv

USC00057167.csv

USC00057337.csv
Downloading https://docs.google.com/uc?export=download&id=15rCwQUxxpH6angDhpXzlvbe1nGetYHrf

USC00057936.csv

USC00058204.csv

USC00058429.csv
USC00059243.csv

USC00068138.csv
USC00080211.csv

USC00084731.csv
USC00088824.csv

USC00098703.csv

USC00100010.csv
USC00100470.csv

USC00105275.csv

USC00106152.csv

USC00107264.csv
USC00108137.csv

USC00110338.csv
USC00112140.csv

USC00112193.csv

USC00112348.csv
USC00112483.csv

USC00113335.csv

USC00114108.csv
USC00114442.csv

USC00114823.csv
USC00115079.csv

USC00115326.csv

USC00115712.csv
USC00115768.csv

USC00115833.csv
USC00115901.csv

USC00115943.csv

USC00116446.csv
USW00003017.csv

Downloading https://docs.google.com/uc?export=download&id=1Tbuom1KMCwHjy7-eexEQcOXSr51i6mae

gzip: stdin: not in gzip format
tar: Child returned status 1
tar: Error is not recoverable: exiting now

This script should save the preprocessed GHCN-D data in ../data path.

---

Pandas DataFrame Basics

Let’s start with an example using pandas DataFrame.

First, let’s read in the comma-seperated GHCN-D dataset for one station at Denver International Airport (DIA), CO (site ID : USW00003017).

To see the list of all available GHCN-D sites and their coordinates and IDs, please see this link.

import os
import pandas as pd

# DIA ghcnd id
site = 'USW00003017'
data_dir = '../data/'


df = pd.read_csv(os.path.join(data_dir, site+'.csv'), parse_dates=['DATE'], index_col=0)

# Display the top five rows of the dataframe
df.head()

	ID	YEAR	MONTH	DAY	TMAX	TMAX_FLAGS	TMIN	TMIN_FLAGS	PRCP	PRCP_FLAGS	...	RHMN_FLAGS	RHMX	RHMX_FLAGS	PSUN	PSUN_FLAGS	LATITUDE	LONGITUDE	ELEVATION	STATE	STATION
DATE
1994-07-20	USW00003017	1994	7	20	316.0	XXS	150.0	XXS	20.0	DXS	...	XXX	NaN	XXX	NaN	XXX	39.8467	-104.6561	1647.1	CO	DENVER INTL AP
1994-07-23	USW00003017	1994	7	23	355.0	XXS	166.0	XXS	0.0	DXS	...	XXX	NaN	XXX	NaN	XXX	39.8467	-104.6561	1647.1	CO	DENVER INTL AP
1994-07-24	USW00003017	1994	7	24	333.0	XXS	155.0	XXS	81.0	DXS	...	XXX	NaN	XXX	NaN	XXX	39.8467	-104.6561	1647.1	CO	DENVER INTL AP
1994-07-25	USW00003017	1994	7	25	327.0	XXS	172.0	XXS	0.0	DXS	...	XXX	NaN	XXX	NaN	XXX	39.8467	-104.6561	1647.1	CO	DENVER INTL AP
1994-07-26	USW00003017	1994	7	26	327.0	XXS	155.0	XXS	0.0	DXS	...	XXX	NaN	XXX	NaN	XXX	39.8467	-104.6561	1647.1	CO	DENVER INTL AP

5 rows × 99 columns

Question: What variables are available?

df.columns

Index(['ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'TMIN',
       'TMIN_FLAGS', 'PRCP', 'PRCP_FLAGS', 'TAVG', 'TAVG_FLAGS', 'SNOW',
       'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'AWND', 'AWND_FLAGS', 'FMTM',
       'FMTM_FLAGS', 'PGTM', 'PGTM_FLAGS', 'WDF2', 'WDF2_FLAGS', 'WDF5',
       'WDF5_FLAGS', 'WSF2', 'WSF2_FLAGS', 'WSF5', 'WSF5_FLAGS', 'WT01',
       'WT01_FLAGS', 'WT02', 'WT02_FLAGS', 'WT08', 'WT08_FLAGS', 'WT16',
       'WT16_FLAGS', 'WT17', 'WT17_FLAGS', 'WT18', 'WT18_FLAGS', 'WT03',
       'WT03_FLAGS', 'WT05', 'WT05_FLAGS', 'WT19', 'WT19_FLAGS', 'WT10',
       'WT10_FLAGS', 'WT09', 'WT09_FLAGS', 'WT06', 'WT06_FLAGS', 'WT07',
       'WT07_FLAGS', 'WT11', 'WT11_FLAGS', 'WT13', 'WT13_FLAGS', 'WT21',
       'WT21_FLAGS', 'WT14', 'WT14_FLAGS', 'WT15', 'WT15_FLAGS', 'WT22',
       'WT22_FLAGS', 'WT04', 'WT04_FLAGS', 'WV03', 'WV03_FLAGS', 'TSUN',
       'TSUN_FLAGS', 'WV01', 'WV01_FLAGS', 'WESD', 'WESD_FLAGS', 'ADPT',
       'ADPT_FLAGS', 'ASLP', 'ASLP_FLAGS', 'ASTP', 'ASTP_FLAGS', 'AWBT',
       'AWBT_FLAGS', 'RHAV', 'RHAV_FLAGS', 'RHMN', 'RHMN_FLAGS', 'RHMX',
       'RHMX_FLAGS', 'PSUN', 'PSUN_FLAGS', 'LATITUDE', 'LONGITUDE',
       'ELEVATION', 'STATE', 'STATION'],
      dtype='object')

The description and units of the dataset is available here.

Operations on pandas DataFrame

pandas DataFrames has several features that give us flexibility to do different calculations and analysis on our dataset. Let’s check some out:

Simple Analysis

For example:

• When was the coldest day at this station during December of last year?

# use python slicing notation inside .loc 
# use idxmin() to find the index of minimum valus
df.loc['2022-12-01':'2022-12-31'].TMIN.idxmin()

Timestamp('2022-12-22 00:00:00')

# Here we easily plot the prior data using matplotlib from pandas
# -- .loc for value based indexing
df.loc['2022-12-01':'2022-12-31'].SNWD.plot(ylabel= 'Daily Average Snow Depth [mm]')

<Axes: xlabel='DATE', ylabel='Daily Average Snow Depth [mm]'>

• How many snow days do we have each year at this station?

Pandas groupby is used for grouping the data according to the categories.

# 1- First select days with snow > 0
# 2- Create a "groupby object" based on the selected columns
# 3- use .size() to compute the size of each group
# 4- sort the values descending 

# we count days where SNOW>0, and sort them and show top 5 years:
df[df['SNOW']>0].groupby('YEAR').size().sort_values(ascending=False).head()

YEAR
2015    36
2019    34
2014    32
2008    32
2007    31
dtype: int64

Or for a more complex analysis:

For example, we have heard that this could be Denver’s first January in 13 years with no 60-degree days.

Below, we show all days with high temperature above 60°F (155.5°C/10) since 2010:

df[(df['MONTH']==1) & (df['YEAR']>=2010) & (df['TMAX']>155.5)].groupby(['YEAR']).size()

YEAR
2011    1
2012    6
2013    4
2014    3
2015    6
2016    1
2017    4
2018    5
2019    3
2020    2
2021    2
2022    3
dtype: int64

This is great! But how big is this dataset for one station?

First, let’s check the file size:

!ls -lh ../data/USW00003017.csv

-rw-r--r-- 1 runner docker 3.6M Feb  5  2023 ../data/USW00003017.csv

Similar to the previous tutorial, we can use the following function to find the size of a variable on memory.

# Define function to display variable size in MB
import sys
def var_size(in_var):
    result = sys.getsizeof(in_var) / 1e6
    print(f"Size of variable: {result:.2f} MB")

var_size(df)

Size of variable: 33.21 MB

Remember, the above rule?

“Have 5 to 10 times as much RAM as the size of your dataset”

Wes McKinney (2017) in 10 things I hate about pandas

So far, we read in and analyzed data for one station. We have a total of +118,000 stations over the world and +4500 stations in Colorado alone!

What if we want to look at the larger dataset?

Scaling up to a larger dataset

Let’s start by reading data from selected stations. The downloaded data for this example includes the climatology observations from 66 selected sites in Colorado.

Pandas can concatenate data to load data spread across multiple files:

!du -csh ../data/*.csv |tail -n1

565M	total

Using a for loop with pandas.concat, we can read multiple files at the same time:

%%time
import glob
co_sites = glob.glob(os.path.join(data_dir, '*.csv'))
df = pd.concat(pd.read_csv(f, index_col=0, parse_dates=['DATE']) for f in co_sites)

CPU times: user 7.62 s, sys: 1.54 s, total: 9.16 s
Wall time: 9.16 s

• How many stations have we read in?

print ("Concatenated data for", len(df.ID.unique()), "unique sites.")

Concatenated data for 66 unique sites.

Now that we concatenated the data for all sites in one DataFrame, we can do similar analysis on it:

• Which site has recorded the most snow days in a year?

%%time
# ~90s on 4GB RAM
snowy_days = df[df['SNOW']>0].groupby(['ID','YEAR']).size()

print ('This site has the highest number of snow days in a year : ')
snowy_days.agg(['idxmax','max'])

This site has the highest number of snow days in a year : 
CPU times: user 365 ms, sys: 0 ns, total: 365 ms
Wall time: 365 ms

idxmax    (USC00052281, 1983)
max                       102
dtype: object

Excersise: Which Colorado site has recorded the most snow days in 2023?

Dask allows us to conceptualize all of these files as a single dataframe!

# Let's do a little cleanup
del df, snowy_days

Computations on Dask DataFrame

Create a “LocalCluster” Client with Dask

from dask.distributed import Client, LocalCluster

cluster = LocalCluster()
client = Client(cluster)
client

Client

Client-7f0d0801-1238-11ef-8a05-00224830bfb1

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Launch dashboard in JupyterLab

Cluster Info

LocalCluster

fa486c12

Dashboard: http://127.0.0.1:8787/status	Workers: 4
Total threads: 4	Total memory: 15.61 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-a8255a81-2b84-4264-a600-5f36bfe9ef7d

Comm: tcp://127.0.0.1:37451	Workers: 4
Dashboard: http://127.0.0.1:8787/status	Total threads: 4
Started: Just now	Total memory: 15.61 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:35541	Total threads: 1
Dashboard: http://127.0.0.1:38561/status	Memory: 3.90 GiB
Nanny: tcp://127.0.0.1:45583
Local directory: /tmp/dask-scratch-space/worker-jj2h0672

Worker: 1

Comm: tcp://127.0.0.1:46587	Total threads: 1
Dashboard: http://127.0.0.1:37533/status	Memory: 3.90 GiB
Nanny: tcp://127.0.0.1:43015
Local directory: /tmp/dask-scratch-space/worker-dtlzybjr

Worker: 2

Comm: tcp://127.0.0.1:39231	Total threads: 1
Dashboard: http://127.0.0.1:38111/status	Memory: 3.90 GiB
Nanny: tcp://127.0.0.1:38233
Local directory: /tmp/dask-scratch-space/worker-bhi2vmmd

Worker: 3

Comm: tcp://127.0.0.1:41827	Total threads: 1
Dashboard: http://127.0.0.1:46289/status	Memory: 3.90 GiB
Nanny: tcp://127.0.0.1:34487
Local directory: /tmp/dask-scratch-space/worker-1wehl4fj

☝️ Click the Dashboard link above.

👈 Or click the “Search” 🔍 button in the dask-labextension dashboard.

Dask DataFrame read_csv to read multiple files

dask.dataframe.read_csv function can be used in conjunction with glob to read multiple csv files at the same time.

Remember we can read one file with pandas.read_csv. For reading multiple files with pandas, we have to concatenate them with pd.concatenate. However, we can read many files at once just using dask.dataframe.read_csv.

Overall, Dask is designed to perform I/O in parallel and is more performant than pandas for operations with multiple files or large files.

%%time
import dask
import dask.dataframe as dd

ddf = dd.read_csv(co_sites, parse_dates=['DATE'])
ddf

CPU times: user 258 ms, sys: 51.6 ms, total: 309 ms
Wall time: 306 ms

Dask DataFrame Structure:

	DATE	ID	YEAR	MONTH	DAY	PRCP	PRCP_FLAGS	TMAX	TMAX_FLAGS	TMIN	TMIN_FLAGS	SNOW	SNOW_FLAGS	WT16	WT16_FLAGS	SNWD	SNWD_FLAGS	WT18	WT18_FLAGS	WT14	WT14_FLAGS	WT01	WT01_FLAGS	TOBS	TOBS_FLAGS	WT04	WT04_FLAGS	WT03	WT03_FLAGS	WT05	WT05_FLAGS	WT11	WT11_FLAGS	WT09	WT09_FLAGS	WT08	WT08_FLAGS	DAPR	DAPR_FLAGS	MDPR	MDPR_FLAGS	WT06	WT06_FLAGS	WT07	WT07_FLAGS	DASF	DASF_FLAGS	MDSF	MDSF_FLAGS	LATITUDE	LONGITUDE	ELEVATION	STATE	STATION
npartitions=66
	datetime64[ns]	string	int64	int64	int64	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	string	float64	float64	float64	string	string
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...

Dask Name: read_csv, 1 expression

ddf.TMAX.mean()

<dask_expr.expr.Scalar: expr=ReadCSV(42a88bf)['TMAX'].mean(), dtype=float64>

Notice that the representation of the DataFrame object contains no data just headers and datatypes. Why?

Lazy Evaluation

Similar to Dask Arrays, Dask DataFrames are lazy. Here the data has not yet been read into the dataframe yet (a.k.a. lazy evaluation).
Dask just construct the task graph of the computation but it will “evaluate” them only when necessary.

So how does Dask know the name and dtype of each column?

Dask has just read the start of the first file and infers the column names and dtypes.

Unlike pandas.read_csv that reads in all files before inferring data types, dask.dataframe.read_csv only reads in a sample from the beginning of the file (or first file if using a glob). The column names and dtypes are then enforced when reading the specific partitions (Dask can make mistakes on these inferences if there is missing or misleading data in the early rows).

Let’s take a look at the start of our dataframe:

ddf.head()

	DATE	ID	YEAR	MONTH	DAY	PRCP	PRCP_FLAGS	TMAX	TMAX_FLAGS	TMIN	...	WT07_FLAGS	DASF	DASF_FLAGS	MDSF	MDSF_FLAGS	LATITUDE	LONGITUDE	ELEVATION	STATE	STATION
0	1895-04-01	USC00114108	1895	4	1	0.0	PX6	NaN	XXX	NaN	...	XXX	NaN	XXX	NaN	XXX	39.1611	-89.4919	192.0	IL	HILLSBORO
1	1895-04-02	USC00114108	1895	4	2	0.0	PX6	NaN	XXX	NaN	...	XXX	NaN	XXX	NaN	XXX	39.1611	-89.4919	192.0	IL	HILLSBORO
2	1895-04-03	USC00114108	1895	4	3	0.0	PX6	NaN	XXX	NaN	...	XXX	NaN	XXX	NaN	XXX	39.1611	-89.4919	192.0	IL	HILLSBORO
3	1895-04-04	USC00114108	1895	4	4	0.0	PX6	NaN	XXX	NaN	...	XXX	NaN	XXX	NaN	XXX	39.1611	-89.4919	192.0	IL	HILLSBORO
4	1895-04-05	USC00114108	1895	4	5	0.0	PX6	NaN	XXX	NaN	...	XXX	NaN	XXX	NaN	XXX	39.1611	-89.4919	192.0	IL	HILLSBORO

5 rows × 54 columns

NOTE: Whenever we operate on our dataframe we read through all of our CSV data so that we don’t fill up RAM. Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible. This enables you to handle larger than memory datasets but, repeated computations will have to load all of the data in each time.

Similar data manipulations as pandas.dataframe can be done for dask.dataframes.
For example, let’s find the highest number of snow days in Colorado:

%%time
print ('This site has the highest number of snow days in a year : ')
snowy_days = ddf[ddf['SNOW']>0].groupby(['ID','YEAR']).size()
snowy_days.compute().agg(['idxmax','max'])

This site has the highest number of snow days in a year : 

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/backends.py:140, in CreationDispatch.register_inplace.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    139 try:
--> 140     return func(*args, **kwargs)
    141 except Exception as e:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/dataframe/io/csv.py:771, in make_reader.<locals>.read(urlpath, blocksize, lineterminator, compression, sample, sample_rows, enforce, assume_missing, storage_options, include_path_column, **kwargs)
    758 def read(
    759     urlpath,
    760     blocksize="default",
   (...)
    769     **kwargs,
    770 ):
--> 771     return read_pandas(
    772         reader,
    773         urlpath,
    774         blocksize=blocksize,
    775         lineterminator=lineterminator,
    776         compression=compression,
    777         sample=sample,
    778         sample_rows=sample_rows,
    779         enforce=enforce,
    780         assume_missing=assume_missing,
    781         storage_options=storage_options,
    782         include_path_column=include_path_column,
    783         **kwargs,
    784     )

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/dataframe/io/csv.py:640, in read_pandas(reader, urlpath, blocksize, lineterminator, compression, sample, sample_rows, enforce, assume_missing, storage_options, include_path_column, **kwargs)
    639 try:
--> 640     head = reader(BytesIO(b_sample), nrows=sample_rows, **head_kwargs)
    641 except pd.errors.ParserError as e:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
   1024 kwds.update(kwds_defaults)
-> 1026 return _read(filepath_or_buffer, kwds)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
    619 # Create the parser.
--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
    622 if chunksize or iterator:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
   1619 self.handles: IOHandles | None = None
-> 1620 self._engine = self._make_engine(f, self.engine)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1898, in TextFileReader._make_engine(self, f, engine)
   1897 try:
-> 1898     return mapping[engine](f, **self.options)
   1899 except Exception:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py:161, in CParserWrapper.__init__(self, src, **kwds)
    160 # error: Cannot determine type of 'names'
--> 161 self._validate_parse_dates_presence(self.names)  # type: ignore[has-type]
    162 self._set_noconvert_columns()

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/base_parser.py:243, in ParserBase._validate_parse_dates_presence(self, columns)
    242 if missing_cols:
--> 243     raise ValueError(
    244         f"Missing column provided to 'parse_dates': '{missing_cols}'"
    245     )
    246 # Convert positions to actual column names

ValueError: Missing column provided to 'parse_dates': 'DATE'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
File <timed exec>:3

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_collection.py:475, in FrameBase.compute(self, fuse, **kwargs)
    473 if not isinstance(out, Scalar):
    474     out = out.repartition(npartitions=1)
--> 475 out = out.optimize(fuse=fuse)
    476 return DaskMethodsMixin.compute(out, **kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_collection.py:590, in FrameBase.optimize(self, fuse)
    572 def optimize(self, fuse: bool = True):
    573     """Optimizes the DataFrame.
    574 
    575     Runs the optimizer with all steps over the DataFrame and wraps the result in a
   (...)
    588         The optimized Dask Dataframe
    589     """
--> 590     return new_collection(self.expr.optimize(fuse=fuse))

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:94, in Expr.optimize(self, **kwargs)
     93 def optimize(self, **kwargs):
---> 94     return optimize(self, **kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:3028, in optimize(expr, fuse)
   3007 """High level query optimization
   3008 
   3009 This leverages three optimization passes:
   (...)
   3024 optimize_blockwise_fusion
   3025 """
   3026 stage: core.OptimizerStage = "fused" if fuse else "simplified-physical"
-> 3028 return optimize_until(expr, stage)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:2979, in optimize_until(expr, stage)
   2976     return result
   2978 # Simplify
-> 2979 expr = result.simplify()
   2980 if stage == "simplified-logical":
   2981     return expr

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:371, in Expr.simplify(self)
    369 while True:
    370     dependents = collect_dependents(expr)
--> 371     new = expr.simplify_once(dependents=dependents, simplified={})
    372     if new._name == expr._name:
    373         break

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:349, in Expr.simplify_once(self, dependents, simplified)
    346 if isinstance(operand, Expr):
    347     # Bandaid for now, waiting for Singleton
    348     dependents[operand._name].append(weakref.ref(expr))
--> 349     new = operand.simplify_once(
    350         dependents=dependents, simplified=simplified
    351     )
    352     simplified[operand._name] = new
    353     if new._name != operand._name:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:322, in Expr.simplify_once(self, dependents, simplified)
    319 expr = self
    321 while True:
--> 322     out = expr._simplify_down()
    323     if out is None:
    324         out = expr

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_groupby.py:595, in Size._simplify_down(self)
    591 def _simplify_down(self):
    592     if (
    593         self._slice is not None
    594         and not isinstance(self._slice, list)
--> 595         or self.frame.ndim == 1
    596     ):
    597         # Scalar slices influence the result and are allowed, i.e., the name of
    598         # the series is different
    599         return
    601     # We can remove every column since pandas reduces to a Series anyway

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:84, in Expr.ndim(self)
     82 @functools.cached_property
     83 def ndim(self):
---> 84     meta = self._meta
     85     try:
     86         return meta.ndim

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:495, in Blockwise._meta(self)
    493 @functools.cached_property
    494 def _meta(self):
--> 495     args = [op._meta if isinstance(op, Expr) else op for op in self._args]
    496     return self.operation(*args, **self._kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:495, in <listcomp>(.0)
    493 @functools.cached_property
    494 def _meta(self):
--> 495     args = [op._meta if isinstance(op, Expr) else op for op in self._args]
    496     return self.operation(*args, **self._kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:2045, in Projection._meta(self)
   2043 @functools.cached_property
   2044 def _meta(self):
-> 2045     if is_dataframe_like(self.frame._meta):
   2046         return super()._meta
   2047     # if we are not a DataFrame and have a scalar, we reduce to a scalar

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/csv.py:85, in ReadCSV._meta(self)
     83 @functools.cached_property
     84 def _meta(self):
---> 85     return self._ddf._meta

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/csv.py:75, in ReadCSV._ddf(self)
     67         meta = self.operation(
     68             self.filename,
     69             header=self.header,
     70             storage_options=self.storage_options,
     71             **kwargs,
     72         )._meta
     73         columns = [list(meta.columns)[0]]
---> 75 return self.operation(
     76     self.filename,
     77     usecols=columns,
     78     header=self.header,
     79     storage_options=self.storage_options,
     80     **kwargs,
     81 )

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/backends.py:142, in CreationDispatch.register_inplace.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    140     return func(*args, **kwargs)
    141 except Exception as e:
--> 142     raise type(e)(
    143         f"An error occurred while calling the {funcname(func)} "
    144         f"method registered to the {self.backend} backend.\n"
    145         f"Original Message: {e}"
    146     ) from e

ValueError: An error occurred while calling the read_csv method registered to the pandas backend.
Original Message: Missing column provided to 'parse_dates': 'DATE'

Nice, but what did Dask do?

# Requires ipywidgets

snowy_days.dask

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    0,
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    0,
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   None,
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    0,
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   None,
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    0,
    10976013,
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   None,
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   True]),
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    0,
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   None,
   True,
   True]),
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  56): (subgraph_callable-e3c2f79736ab5ae61e614683e1f7c7a5, [(<function dask.bytes.core.read_block_from_file(lazy_file, off, bs, delimiter)>,
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    0,
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    0,
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   None,
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  58): (subgraph_callable-e3c2f79736ab5ae61e614683e1f7c7a5, [(<function dask.bytes.core.read_block_from_file(lazy_file, off, bs, delimiter)>,
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    0,
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    0,
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   None,
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    0,
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   None,
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    0,
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   None,
   True,
   True]),
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  62): (subgraph_callable-e3c2f79736ab5ae61e614683e1f7c7a5, [(<function dask.bytes.core.read_block_from_file(lazy_file, off, bs, delimiter)>,
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    0,
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   None,
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   True]),
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  63): (subgraph_callable-e3c2f79736ab5ae61e614683e1f7c7a5, [(<function dask.bytes.core.read_block_from_file(lazy_file, off, bs, delimiter)>,
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    0,
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   None,
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 ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
  64): (subgraph_callable-e3c2f79736ab5ae61e614683e1f7c7a5, [(<function dask.bytes.core.read_block_from_file(lazy_file, off, bs, delimiter)>,
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    0,
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   None,
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   True]),
 ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
  65): (subgraph_callable-e3c2f79736ab5ae61e614683e1f7c7a5, [(<function dask.bytes.core.read_block_from_file(lazy_file, off, bs, delimiter)>,
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    0,
    8779188,
    b'\n'),
   None,
   True,
   True])}

You can also view the underlying task graph using .visualize():

#graph is too large
snowy_days.visualize()

Use .compute wisely!

Share intermediate results

For most operations, dask.dataframe hashes the arguments, allowing duplicate computations to be shared, and only computed once.

For example, let’s compute the mean and standard deviation for Maximum daily temperature of all snow days.

snowy_days = ddf[ddf['SNOW']>0]
mean_tmax = snowy_days.TMAX.mean()
std_tmax = snowy_days.TMAX.std()

%%time

mean_tmax_result = mean_tmax.compute()
std_tmax_result = std_tmax.compute()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/backends.py:140, in CreationDispatch.register_inplace.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    139 try:
--> 140     return func(*args, **kwargs)
    141 except Exception as e:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/dataframe/io/csv.py:771, in make_reader.<locals>.read(urlpath, blocksize, lineterminator, compression, sample, sample_rows, enforce, assume_missing, storage_options, include_path_column, **kwargs)
    758 def read(
    759     urlpath,
    760     blocksize="default",
   (...)
    769     **kwargs,
    770 ):
--> 771     return read_pandas(
    772         reader,
    773         urlpath,
    774         blocksize=blocksize,
    775         lineterminator=lineterminator,
    776         compression=compression,
    777         sample=sample,
    778         sample_rows=sample_rows,
    779         enforce=enforce,
    780         assume_missing=assume_missing,
    781         storage_options=storage_options,
    782         include_path_column=include_path_column,
    783         **kwargs,
    784     )

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/dataframe/io/csv.py:640, in read_pandas(reader, urlpath, blocksize, lineterminator, compression, sample, sample_rows, enforce, assume_missing, storage_options, include_path_column, **kwargs)
    639 try:
--> 640     head = reader(BytesIO(b_sample), nrows=sample_rows, **head_kwargs)
    641 except pd.errors.ParserError as e:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
   1024 kwds.update(kwds_defaults)
-> 1026 return _read(filepath_or_buffer, kwds)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
    619 # Create the parser.
--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
    622 if chunksize or iterator:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
   1619 self.handles: IOHandles | None = None
-> 1620 self._engine = self._make_engine(f, self.engine)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1898, in TextFileReader._make_engine(self, f, engine)
   1897 try:
-> 1898     return mapping[engine](f, **self.options)
   1899 except Exception:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py:161, in CParserWrapper.__init__(self, src, **kwds)
    160 # error: Cannot determine type of 'names'
--> 161 self._validate_parse_dates_presence(self.names)  # type: ignore[has-type]
    162 self._set_noconvert_columns()

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/base_parser.py:243, in ParserBase._validate_parse_dates_presence(self, columns)
    242 if missing_cols:
--> 243     raise ValueError(
    244         f"Missing column provided to 'parse_dates': '{missing_cols}'"
    245     )
    246 # Convert positions to actual column names

ValueError: Missing column provided to 'parse_dates': 'DATE'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
File <timed exec>:1

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_collection.py:475, in FrameBase.compute(self, fuse, **kwargs)
    473 if not isinstance(out, Scalar):
    474     out = out.repartition(npartitions=1)
--> 475 out = out.optimize(fuse=fuse)
    476 return DaskMethodsMixin.compute(out, **kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_collection.py:590, in FrameBase.optimize(self, fuse)
    572 def optimize(self, fuse: bool = True):
    573     """Optimizes the DataFrame.
    574 
    575     Runs the optimizer with all steps over the DataFrame and wraps the result in a
   (...)
    588         The optimized Dask Dataframe
    589     """
--> 590     return new_collection(self.expr.optimize(fuse=fuse))

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:94, in Expr.optimize(self, **kwargs)
     93 def optimize(self, **kwargs):
---> 94     return optimize(self, **kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:3028, in optimize(expr, fuse)
   3007 """High level query optimization
   3008 
   3009 This leverages three optimization passes:
   (...)
   3024 optimize_blockwise_fusion
   3025 """
   3026 stage: core.OptimizerStage = "fused" if fuse else "simplified-physical"
-> 3028 return optimize_until(expr, stage)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:2979, in optimize_until(expr, stage)
   2976     return result
   2978 # Simplify
-> 2979 expr = result.simplify()
   2980 if stage == "simplified-logical":
   2981     return expr

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:371, in Expr.simplify(self)
    369 while True:
    370     dependents = collect_dependents(expr)
--> 371     new = expr.simplify_once(dependents=dependents, simplified={})
    372     if new._name == expr._name:
    373         break

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:349, in Expr.simplify_once(self, dependents, simplified)
    346 if isinstance(operand, Expr):
    347     # Bandaid for now, waiting for Singleton
    348     dependents[operand._name].append(weakref.ref(expr))
--> 349     new = operand.simplify_once(
    350         dependents=dependents, simplified=simplified
    351     )
    352     simplified[operand._name] = new
    353     if new._name != operand._name:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:349, in Expr.simplify_once(self, dependents, simplified)
    346 if isinstance(operand, Expr):
    347     # Bandaid for now, waiting for Singleton
    348     dependents[operand._name].append(weakref.ref(expr))
--> 349     new = operand.simplify_once(
    350         dependents=dependents, simplified=simplified
    351     )
    352     simplified[operand._name] = new
    353     if new._name != operand._name:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:332, in Expr.simplify_once(self, dependents, simplified)
    330 # Allow children to simplify their parents
    331 for child in expr.dependencies():
--> 332     out = child._simplify_up(expr, dependents)
    333     if out is None:
    334         out = expr

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/io.py:76, in BlockwiseIO._simplify_up(self, parent, dependents)
     72 def _simplify_up(self, parent, dependents):
     73     if (
     74         self._absorb_projections
     75         and isinstance(parent, Projection)
---> 76         and is_dataframe_like(self._meta)
     77     ):
     78         # Column projection
     79         parent_columns = parent.operand("columns")
     80         proposed_columns = determine_column_projection(self, parent, dependents)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/csv.py:85, in ReadCSV._meta(self)
     83 @functools.cached_property
     84 def _meta(self):
---> 85     return self._ddf._meta

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/csv.py:75, in ReadCSV._ddf(self)
     67         meta = self.operation(
     68             self.filename,
     69             header=self.header,
     70             storage_options=self.storage_options,
     71             **kwargs,
     72         )._meta
     73         columns = [list(meta.columns)[0]]
---> 75 return self.operation(
     76     self.filename,
     77     usecols=columns,
     78     header=self.header,
     79     storage_options=self.storage_options,
     80     **kwargs,
     81 )

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/backends.py:142, in CreationDispatch.register_inplace.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    140     return func(*args, **kwargs)
    141 except Exception as e:
--> 142     raise type(e)(
    143         f"An error occurred while calling the {funcname(func)} "
    144         f"method registered to the {self.backend} backend.\n"
    145         f"Original Message: {e}"
    146     ) from e

ValueError: An error occurred while calling the read_csv method registered to the pandas backend.
Original Message: Missing column provided to 'parse_dates': 'DATE'

But if we pass both arguments in a single .compute, we can share the intermediate results:

%%time
mean_tmax_result, std_tmax_result = dask.compute(mean_tmax, std_tmax)

CPU times: user 736 ms, sys: 73.6 ms, total: 809 ms
Wall time: 5.53 s

Here using dask.compute only one allowed sharing intermediate results between TMAX mean and median calculations and improved total performance.

mean_tmax.dask

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  0): (<function dask.dataframe.methods.mean_aggregate(s, n)>, ('sum-tree-2a9c4572a277c221b4aaa85d00d087c4',
   0), ('count-tree-58e5c1efd576ba0e4cd805b9c5535da1', 0)),
 ('count-tree-58e5c1efd576ba0e4cd805b9c5535da1',
  0): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.aggregate of <class 'dask_expr._reductions.Count'>>, [[('chunk-ed4c94719b794cd7d3702956a04062af',
     0),
    ('chunk-ed4c94719b794cd7d3702956a04062af', 1),
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    ('chunk-ed4c94719b794cd7d3702956a04062af', 16),
    ('chunk-ed4c94719b794cd7d3702956a04062af', 17),
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    ('chunk-ed4c94719b794cd7d3702956a04062af', 19),
    ('chunk-ed4c94719b794cd7d3702956a04062af', 20),
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    ('chunk-ed4c94719b794cd7d3702956a04062af', 22),
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  19): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   19), 'TMAX'),
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  20): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   20), 'TMAX'),
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  21): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   21), 'TMAX'),
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  22): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   22), 'TMAX'),
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  23): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   23), 'TMAX'),
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  24): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   24), 'TMAX'),
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  25): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   25), 'TMAX'),
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  26): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   26), 'TMAX'),
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  27): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   27), 'TMAX'),
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  28): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   28), 'TMAX'),
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  29): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   29), 'TMAX'),
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  30): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   30), 'TMAX'),
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  31): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   31), 'TMAX'),
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  32): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   32), 'TMAX'),
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  33): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   33), 'TMAX'),
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  34): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   34), 'TMAX'),
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  35): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   35), 'TMAX'),
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  36): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   36), 'TMAX'),
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  37): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   37), 'TMAX'),
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  38): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   38), 'TMAX'),
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  39): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   39), 'TMAX'),
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  40): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   40), 'TMAX'),
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  41): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   41), 'TMAX'),
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  42): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   42), 'TMAX'),
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  43): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   43), 'TMAX'),
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  44): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   44), 'TMAX'),
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  45): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   45), 'TMAX'),
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  46): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   46), 'TMAX'),
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  47): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   47), 'TMAX'),
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  48): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   48), 'TMAX'),
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  49): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   49), 'TMAX'),
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  50): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   50), 'TMAX'),
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  51): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   51), 'TMAX'),
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  52): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   52), 'TMAX'),
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  53): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   53), 'TMAX'),
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  54): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   54), 'TMAX'),
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  55): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   55), 'TMAX'),
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  56): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   56), 'TMAX'),
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  57): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   57), 'TMAX'),
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  58): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   58), 'TMAX'),
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  59): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   59), 'TMAX'),
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  60): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   60), 'TMAX'),
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  61): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   61), 'TMAX'),
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  62): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   62), 'TMAX'),
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  63): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   63), 'TMAX'),
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  64): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   64), 'TMAX'),
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  65): (<function _operator.getitem(a, b, /)>, ('getitem-5cdbb5fd359cd17c95b7fd5d8fca39d8',
   65), 'TMAX'),
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  0): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   0), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 0)),
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  1): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   1), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 1)),
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  2): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   2), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 2)),
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  3): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   3), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 3)),
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  4): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   4), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 4)),
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  5): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   5), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 5)),
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  6): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   6), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 6)),
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  7): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   7), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 7)),
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  8): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   8), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 8)),
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  9): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   9), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 9)),
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  10): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   10), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 10)),
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  11): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   11), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 11)),
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  12): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   12), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 12)),
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  13): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   13), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 13)),
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  14): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   14), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 14)),
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  15): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   15), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 15)),
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  16): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   16), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 16)),
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  17): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   17), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 17)),
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  18): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   18), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 18)),
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  19): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   19), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 19)),
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  20): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   20), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 20)),
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  21): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   21), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 21)),
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  22): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   22), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 22)),
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  23): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   23), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 23)),
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  24): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   24), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 24)),
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  25): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   25), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 25)),
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  26): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   26), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 26)),
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  27): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   27), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 27)),
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  28): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   28), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 28)),
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  29): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   29), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 29)),
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  30): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   30), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 30)),
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  31): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   31), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 31)),
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  32): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   32), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 32)),
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  33): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   33), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 33)),
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  34): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   34), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 34)),
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  35): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   35), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 35)),
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  36): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   36), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 36)),
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  37): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   37), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 37)),
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  38): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   38), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 38)),
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  39): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   39), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 39)),
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  40): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
   40), ('gt-c8f54ed5ef99006c992ae82bb77348f3', 40)),
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  41): (<function _operator.getitem(a, b, /)>, ('read_csv-5c8749b5b396203c0da6405bc42a88bf',
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    55)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  56): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    56)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  57): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    57)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  58): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    58)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  59): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    59)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  60): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    60)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  61): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    61)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  62): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    62)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  63): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    63)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  64): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    64)], {'skipna': True, 'numeric_only': False, 'axis': 0}),
 ('chunk-29b0e82d11232c3bfe5aa3782379cb0d',
  65): (<function dask.utils.apply(func, args, kwargs=None)>, <bound method Reduction.chunk of <class 'dask_expr._reductions.Sum'>>, [('getitem-1d7a11638ed0b288749294bfe2ed7aac',
    65)], {'skipna': True, 'numeric_only': False, 'axis': 0})}

Here some operations such as the calls to read the csv files, the filtering, and the grouping is exactly similar between both operations, so they can share intermediate results. Remember, Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible.

.persist or caching

Sometimes you might want your computers to keep intermediate results in memory, if it fits in the memory.

The .persist() method can be used to “cache” data and tell Dask what results to keep around. You should only use .persist() with any data or computation that fits in memory.

For example, if we want to only do analysis on a subset of data (for example snow days at Boulder site):

boulder_snow = ddf[(ddf['SNOW']>0)&(ddf['ID']=='USC00050848')]

%%time
tmax = boulder_snow.TMAX.mean().compute()
tmin = boulder_snow.TMIN.mean().compute()

print (tmin, tmax)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/backends.py:140, in CreationDispatch.register_inplace.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    139 try:
--> 140     return func(*args, **kwargs)
    141 except Exception as e:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/dataframe/io/csv.py:771, in make_reader.<locals>.read(urlpath, blocksize, lineterminator, compression, sample, sample_rows, enforce, assume_missing, storage_options, include_path_column, **kwargs)
    758 def read(
    759     urlpath,
    760     blocksize="default",
   (...)
    769     **kwargs,
    770 ):
--> 771     return read_pandas(
    772         reader,
    773         urlpath,
    774         blocksize=blocksize,
    775         lineterminator=lineterminator,
    776         compression=compression,
    777         sample=sample,
    778         sample_rows=sample_rows,
    779         enforce=enforce,
    780         assume_missing=assume_missing,
    781         storage_options=storage_options,
    782         include_path_column=include_path_column,
    783         **kwargs,
    784     )

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/dataframe/io/csv.py:640, in read_pandas(reader, urlpath, blocksize, lineterminator, compression, sample, sample_rows, enforce, assume_missing, storage_options, include_path_column, **kwargs)
    639 try:
--> 640     head = reader(BytesIO(b_sample), nrows=sample_rows, **head_kwargs)
    641 except pd.errors.ParserError as e:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
   1024 kwds.update(kwds_defaults)
-> 1026 return _read(filepath_or_buffer, kwds)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
    619 # Create the parser.
--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
    622 if chunksize or iterator:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
   1619 self.handles: IOHandles | None = None
-> 1620 self._engine = self._make_engine(f, self.engine)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1898, in TextFileReader._make_engine(self, f, engine)
   1897 try:
-> 1898     return mapping[engine](f, **self.options)
   1899 except Exception:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py:161, in CParserWrapper.__init__(self, src, **kwds)
    160 # error: Cannot determine type of 'names'
--> 161 self._validate_parse_dates_presence(self.names)  # type: ignore[has-type]
    162 self._set_noconvert_columns()

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/pandas/io/parsers/base_parser.py:243, in ParserBase._validate_parse_dates_presence(self, columns)
    242 if missing_cols:
--> 243     raise ValueError(
    244         f"Missing column provided to 'parse_dates': '{missing_cols}'"
    245     )
    246 # Convert positions to actual column names

ValueError: Missing column provided to 'parse_dates': 'DATE'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
File <timed exec>:1

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_collection.py:475, in FrameBase.compute(self, fuse, **kwargs)
    473 if not isinstance(out, Scalar):
    474     out = out.repartition(npartitions=1)
--> 475 out = out.optimize(fuse=fuse)
    476 return DaskMethodsMixin.compute(out, **kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_collection.py:590, in FrameBase.optimize(self, fuse)
    572 def optimize(self, fuse: bool = True):
    573     """Optimizes the DataFrame.
    574 
    575     Runs the optimizer with all steps over the DataFrame and wraps the result in a
   (...)
    588         The optimized Dask Dataframe
    589     """
--> 590     return new_collection(self.expr.optimize(fuse=fuse))

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:94, in Expr.optimize(self, **kwargs)
     93 def optimize(self, **kwargs):
---> 94     return optimize(self, **kwargs)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:3028, in optimize(expr, fuse)
   3007 """High level query optimization
   3008 
   3009 This leverages three optimization passes:
   (...)
   3024 optimize_blockwise_fusion
   3025 """
   3026 stage: core.OptimizerStage = "fused" if fuse else "simplified-physical"
-> 3028 return optimize_until(expr, stage)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_expr.py:2979, in optimize_until(expr, stage)
   2976     return result
   2978 # Simplify
-> 2979 expr = result.simplify()
   2980 if stage == "simplified-logical":
   2981     return expr

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:371, in Expr.simplify(self)
    369 while True:
    370     dependents = collect_dependents(expr)
--> 371     new = expr.simplify_once(dependents=dependents, simplified={})
    372     if new._name == expr._name:
    373         break

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:349, in Expr.simplify_once(self, dependents, simplified)
    346 if isinstance(operand, Expr):
    347     # Bandaid for now, waiting for Singleton
    348     dependents[operand._name].append(weakref.ref(expr))
--> 349     new = operand.simplify_once(
    350         dependents=dependents, simplified=simplified
    351     )
    352     simplified[operand._name] = new
    353     if new._name != operand._name:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:349, in Expr.simplify_once(self, dependents, simplified)
    346 if isinstance(operand, Expr):
    347     # Bandaid for now, waiting for Singleton
    348     dependents[operand._name].append(weakref.ref(expr))
--> 349     new = operand.simplify_once(
    350         dependents=dependents, simplified=simplified
    351     )
    352     simplified[operand._name] = new
    353     if new._name != operand._name:

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/_core.py:332, in Expr.simplify_once(self, dependents, simplified)
    330 # Allow children to simplify their parents
    331 for child in expr.dependencies():
--> 332     out = child._simplify_up(expr, dependents)
    333     if out is None:
    334         out = expr

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/io.py:76, in BlockwiseIO._simplify_up(self, parent, dependents)
     72 def _simplify_up(self, parent, dependents):
     73     if (
     74         self._absorb_projections
     75         and isinstance(parent, Projection)
---> 76         and is_dataframe_like(self._meta)
     77     ):
     78         # Column projection
     79         parent_columns = parent.operand("columns")
     80         proposed_columns = determine_column_projection(self, parent, dependents)

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/csv.py:85, in ReadCSV._meta(self)
     83 @functools.cached_property
     84 def _meta(self):
---> 85     return self._ddf._meta

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask_expr/io/csv.py:75, in ReadCSV._ddf(self)
     67         meta = self.operation(
     68             self.filename,
     69             header=self.header,
     70             storage_options=self.storage_options,
     71             **kwargs,
     72         )._meta
     73         columns = [list(meta.columns)[0]]
---> 75 return self.operation(
     76     self.filename,
     77     usecols=columns,
     78     header=self.header,
     79     storage_options=self.storage_options,
     80     **kwargs,
     81 )

File ~/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/dask/backends.py:142, in CreationDispatch.register_inplace.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    140     return func(*args, **kwargs)
    141 except Exception as e:
--> 142     raise type(e)(
    143         f"An error occurred while calling the {funcname(func)} "
    144         f"method registered to the {self.backend} backend.\n"
    145         f"Original Message: {e}"
    146     ) from e

ValueError: An error occurred while calling the read_csv method registered to the pandas backend.
Original Message: Missing column provided to 'parse_dates': 'DATE'

boulder_snow = ddf[(ddf['SNOW']>0)&(ddf['ID']=='USC00050848')].persist()

%%time

tmax = boulder_snow.TMAX.mean().compute()
tmin = boulder_snow.TMIN.mean().compute()
print (tmin, tmax)

-74.82074711099168 37.419103836866114
CPU times: user 624 ms, sys: 93.3 ms, total: 717 ms
Wall time: 4.87 s

As you can see the analysis on this persisted data is much faster because we are not repeating the loading and selecting.

Dask DataFrames Best Practices

Use pandas (when you can)

For data that fits into RAM, pandas can often be easier and more efficient to use than Dask DataFrame. However, Dask DataFrame is a powerful tool for larger-than-memory datasets.

When the data is still larger than memory, Dask DataFrame can be used to reduce the larger datasets to a manageable level that pandas can handle. Next, use pandas at that point.

Avoid Full-Data Shuffling

Some operations are more expensive to compute in a parallel setting than if they are in-memory on a single machine (for example, set_index or merge). In particular, shuffling operations that rearrange data can become very communication intensive.

pandas performance tips

pandas performance tips such as using vectorized operations also apply to Dask DataFrames. See Modern Pandas notebook for more tips on better performance with pandas.

Check Partition Size

Similar to chunks, partitions should be small enough that they fit in the memory, but large enough to avoid that the communication overhead.

blocksize

• The number of partitions can be set using the blocksize argument. If none is given, the number of partitions/blocksize is calculated depending on the available memory and the number of cores on a machine up to a max of 64 MB. As we increase the blocksize, the number of partitions (calculated by Dask) will decrease. This is especially important when reading one large csv file.

As a good rule of thumb, you should aim for partitions that have around 100MB of data each.

Smart use of .compute()

Try avoiding running .compute() operation as long as possible. Dask works best when users avoid computation until results are needed. The .compute() command informs Dask to trigger computations on the Dask DataFrame.
As shown in the above example, the intermediate results can also be shared by calling .compute() only once.

Close your local Dask Cluster

It is always a good practice to close the Dask cluster you created.

client.shutdown()

---

Summary

In this notebook, we have learned about:

• Dask DataFrame concept and component.

• When to use and when to avoid Dask DataFrames?

• How to use Dask DataFrame?

• Some best practices around Dask DataFrames.

Resources and references

• Reference

  • Dask Docs

  • Dask Examples

  • Dask Code

  • Dask Blog

  • Pandas Docs

• Ask for help

  • dask tag on Stack Overflow, for usage questions

  • github discussions: dask for general, non-bug, discussion, and usage questions

  • github issues: dask for bug reports and feature requests