filoc 0.0.24

eases the saving and reading of files within a structured folder tree

Filoc is a highly customizable library that primarily enables you to:

• Visualize the content of a set of files as a pandas DataFrame
• Save a DataFrame into a set of files

The set of files is defined by a format string where the placeholders are part of the data. Consider the following format string:

/data/{country}/{company}/info.json

You see two placeholders, namely country and company. Both are part of the data read and saved by filoc. Let's say that the info.json files contain two additional attributes address and phone, then filoc works as a bidirectional binding between the files and a DataFrame with the following columns:

	country	company	address	phone
...	...	...	...	...

This is the key feature of filoc, which enables you to choose the best path structure for your needs and at the same time to manipulate the whole data set in a single DataFrame!

Filoc is highly customizable: You can work with any type of files (builtins: json, yaml, csv, pickle) on any file system (local, ftp, sftp, http, dropbox, google storage, google drive, hadoop, azure data storages, samba). You can even replace the pandas DataFrame by an alternative "frontend" if you need (builtins: pandas and json).

Use Cases (Jupyter Notebook)

You can get a concrete and practical insight into filoc in the following show-case notebooks:

Machine Learning Workflow with filoc

Covid-19 Data Analysis from the John Hopkins University Github repository

Basic example

Install

First of all, you need to install the filoc library:

pip install filoc

Import

In most scenarios, you only need to import the filoc(...) factory function:

from filoc import filoc

This is the most pythonic way to use filoc, but you can also use alternative factories to improve IDE static analysis, namely filoc_json(...) and filoc_pandas(...).

Create a Filoc instance

Let's create a Filoc instance to work with set of files previously defined by the format path /data/{country}/{company}/info.json:

loc = filoc('/data/{country}/{company}/info.json')

Read all files

You read the whole set of file as follows:

df = loc.read_contents()

print(df)

# OUTPUT
#        country  company   address  phone          
#  ----  -------  --------  -------  -------------- 
#  0     France   OVH       Roubaix  [phone]   
#  1     Germany  Strato    Berlin   [phone]   
#  2     Germany  DF        Munich   [phone] 

Read a subset of files

Instead of reading all the files, you can restrict the reading to a subset of files by adding conditions:

df = loc.read_contents(country='Germany')

print(df)

# OUTPUT
#        country  company  address  phone          
#  ----  -------  --------  -------  -------------- 
#  0     Germany  Strato    Berlin   [phone]   
#  1     Germany  DF        Munich   [phone] 

Write to the set of files

Filoc instance are by default readonly. We need to create a writable Filoc:

# The filoc need to be initialized as writable
loc = filoc('/data/{country}/{company}/info.json', writable=True)

Now, let's fix the address of the DF company and save the result:

# Change the address
df.loc[df['company'] == 'DF', 'address'] = 'Ismaning (by Munich)' 

# Save the change
loc.write_contents(df)

Let's see with a linux shell, that the file was properly updated:

> cat /data/Germany/DF/info.json

{
  "address": "Ismaning (by Munich)",
  "phone": "[phone]"
}

Working with a single entry

Sometimes, it is convenient to focus your work on a single row of the data set. Filoc allows you to work with a pandas Series instead of a DataFrame. The following table shows the filoc functions in relation to respectively DataFrame and Series:

cardinality	read	write	frontend class
1	loc.read_content()	loc.write_content()	Series
*	loc.read_contents()	loc.write_contents()	DataFrame

Here an example of how to use the Series related functions:

# ---- read ----
series = loc.read_content(country='Germany', company='DF')
print(series)

# OUTPUT
# country               Germany
# company                    DF
# address  Ismaning (by Munich)
# phone          [phone]
# dtype: object

print(f'The company address is: {series.phone}')

# OUTPUT
# The company address is: Munich

# Update the phone number and save back the change
series.phone = "+49 (0)89/998288026"
loc.write_content(series)

Typed placeholders

A format placeholder can be typed to map to a specific python type. Filoc use a minimal subset of format string syntax:

'{value}'    # 'abc' is parsed to string 'abc'
'{value:d}'  # '-123' is parsed to integer -123
'{value:g}'  # '3.5' is parsed to float 3.5

Local and remote files

Under the hood, filoc accesses the files by using the fsspec library. It enables filoc to work with the following file systems:

Protocol	File system	Additional requirements
(none) or file://	local
memory://	memory
zip://	zip
ftp://	ftp
cached:// or blockcache://	blockwise caching pseudo
filecache://	whole file caching pseudo
simplecache://	simple caching pseudo
dropbox://	dropbox	dropboxdrivefs, requests, dropbox
http:// or https://	http	requests, aiohttp
gcs:// or gs://	google storage	gcsfs
gdrive://	google drive	gdrivefs
sftp:// or ssh://	ssh	paramiko
hdfs://	hadoop	pyarrow and local java libraries required for HDFS
webhdfs://	hadoop over HTTP	requests
s3://	S3	s3fs
adl://	azure datalake gen1	adlfs
abfs:// or az://	azure datalake gen2 + blob storage	adlfs
dask://	dask worker	dask
github://	github	requests
git://	git	pygit2
smb://	SMB	smbprotocol or smbprotocol[kerberos]
jupyter:// or jlab://	jupyter	requests

Here is a example, how to use github:// to read the covid statistics from the Johns Hopkins University github repository.

Composite

Filoc instances can be joined together into a "composite filoc". The simplest syntax for that is to replace the single format path by a keyed list of paths:

mloc = filoc({
    'contact' : '/data/contact/{country}/{company}/info.json',
    'finance' : '/data/finance/{country}/{company}/{year:d}_revenue.json'
})

The contact and finance keys are the name of the sub-filocs.

The alternative syntax consists in instantiating manually the sub-filocs:

mloc = filoc({
    'contact' : contact_loc,
    'finance' : filoc('/data/finance/{country}/{company}/{year:d}_revenue.json', writable=True)
})

The alternative syntax is especially important, if you need to override the configuration for a specific "sub-filoc". In the previous example, the second "sub-filoc" 'finance' is declared "writable", whereas the first one remains readonly.

Now, see how such a composite filoc works:

df = mloc.read_contents() 

print(df)

# OUTPUT
#   shared.country  shared.company  shared.year      contact.address   contact.phone finance.revenue
#   -------------  -------------  ---------- --------------------  -------------- ---------------
# 0        France            OVH        2019              Roubaix    [phone]        10256745
# 1       Germany             DF        2019 Ismaning (by Munich)  [phone]        14578415
# 2       Germany         Strato        2019               Berlin    [phone]        54657631
# 3        France            OVH        2020              Roubaix    [phone]        11132643
# 4       Germany             DF        2020 Ismaning (by Munich)  [phone]        37456466
# 5       Germany         Strato        2020               Berlin    [phone]        54411544

Filoc joins the data from the two set of files together. It uses the format placeholders from the format path as join keys, to match and join the rows together from the both set of files. The shared keys are prefixed by 'shared.' whereas the attributes found in the files themselves are prefixed by the named of the filoc.

In this example, we have set the finance filoc writable, so we can edit the dataframe and save back the result:

df.loc[ (df['shared.year'] == 2019) & (df['shared.company'] == 'OVH'), 'finance.revenue'] = 0

mloc.write_contents(df)

We check the updated file content:

$> cat /data/France/OVH/2019_revenue.json
{
  "revenue": 0
}

Backend

Filoc backend is the part of the implementation, that processes the files. You define the backend via the backend argument of the filoc(...) factory:

loc = filoc(..., backend='yaml')

Builtin backends

Filoc has four builtin backends:

Name	Description	option singleton	option encoding
json	json files	Yes	Yes
yaml	yaml files	Yes	Yes
csv	csv files	No	Yes
pickle	pickle files	Yes	No

• Option singleton: If True, then filoc reads and writes a single object in each file (Mapping). If False the filoc reads and writes lists of object (List of Mapping).
• Option encoding: Configure the encoding of the file read and written by filoc.

Custom backends

You can also work with custom files and perform custom pre-processing to the files, by passing a custom instance of the BackendContract contract.

Frontend

Filoc frontend is the part of the implementation, that transforms the file content to a python object, namely by default a DataFrame (returned by read_contents(...)) or a Series (returned by read_content(..)).

Builtin frontends

Filoc has two builtin frontends:

cardinality	read	write	frontend class
1	loc.read_content()	loc.write_content()	Dict[str, Any]
*	loc.read_contents()	loc.write_contents()	List[Dict[str, Any]]

Custom frontends

You can work with custom frontend objects, by passing a custom instance of the FrontendContract contract.

Caching

The filoc(...) factory accepts a cache_locpath and cache_fs arguments. This feature is particularly useful when you work on remote file system or when the backend processes a large amount of data. The cache is invalidated when the path timestamp has changed on the file system.

The cache_locpath may contain format placeholders. In that case, the cache is split into multiple files basedd on the placeholder values. This features allows to "encapsulate" the cache data in the same folder as the original data, or in the same folder structure as the original data.

Example:

loc = filoc('github://user:rep/data/{country}/{company}/info.json', cache_locpath='/cache/{country}/cache.dat')

Locking

A simple locking mechanism working on local and remote file systems allows you to synchronize the reading and writing of files:

with loc.lock():
    series = loc.read_content(country='Germany', company='DF')
    series.phone = "+49 (0)89/998288026"
    loc.write_content(series)

In this example, the reading and writing is garanteed to be concurrent safe.

The locking mechanism consists of writing a lock file on the file system: It means that the protection only works against concurrent accesses that use the same call convention inside the Filoc.lock() statement.