count-api 2.0.9

API client for count.co

Count API Documentation V2.0

Harness the exploration, collaboration, and visualisation capabilities of the count.co web service in your workflow with the Count API package for Python.

This project contains an API client for the count.co web service, providing methods for authentication, data upload, and data visualisation.

Requirements

Python 2 or 3

Optimised for Jupyter Notebook environment

Packages installed as dependencies:

• requests
• protobuf
• PyHamcrest

Access

In order to use the Count API module, you need to generate a token in your accounts page on count.co.

Installation

In your terminal, you can use pip to install count:

• pip install count_api
• pip3 install count_api

Import

from count_api import CountAPI

Initialise

token = "TOKEN GENERATED FROM COUNT.CO ADMIN PAGE"
count = CountAPI()
count.set_api_token(token)

Load data to Count

Tables

In the Count API, tables are objects that represent the data you are sending to Count.

#Upload a File (.csv or .txt)
path = 'Users/me/Downloads/norweiganCarSales.csv'
table = count.upload(path=path, name='Car Sales')

#Upload a dataframe
df = pd.read_csv('norweiganCarSales.csv')
table = count.upload(data=df.to_csv(), name='Car Sales')

Previewing table contents

The first n (default 10, max 100) lines of a table can be printed to screen by using the table.head(n) method, e.g.

table.head()

You can also find the number of rows in a column via

table.size()

which returns a tuple of ints (number_of_rows, number_of_columns).

Generate Count URL

If you want to visualise your table in Count, you'll need the URL. See the example scripts on examples of how to best use this URL to distribute to your team, and visualise for yourself.

url = table.url()

Create a Visual

Selecting columns

To create a visual, you will need reference the columns in the Table object. CountAPI provides several ways to reference a column in the table:

q_columns = table.columns('Quantity') # List of Column objects for columns with title matching string 'Quantity'
q_column  = table['Quantity']         # Column object of first column with title matching string 'Quantity'
col_type = table.column('Type')       # Column object of first column with title matching string 'Type'
first_column = table[0]               # Column object of first column in table
first_column = table.column(0)        # Column object of first column in table

Filters

from datetime import datetime
filter_year = table['Year'].filter('>=',datetime(2007,1,1)).filter('<=',datetime(2016,12,31))

[states_filter] = table['state'].filter('IN','Florida').filter('IN', ['California','Colorado'])

first_column_filter = table[0].filter('>', 10)

Operator options:

• For strings:
  • 'IN'
• For numbers:
  • '>', '>=', '<', '<=', '!=', '='

Select aggregates

Aggregates can be selected for a particular chart-axis (x, y, color, or size) via the aggregates dict parameter of table.upload_visual. For example, to selecting aggregate 'sum' on the y-axis, the aggregates dict parameter would read

aggregates = {'y' : 'sum'}

Aggregate options (case insensitive):

• For strings:
  • distinct, min, max
• For numbers and datetimes:
  • distinct, min, max, med, sum, avg, med

Set chart options

Chart options can be set using the chart_options dict parameter of table.upload_visual. Options that can be set include

• x_type, y_type, size_type, color_type: set scale of the axis to be either linear or log e.g. {x_type: linear}. Note that this cannot be set for an axis representing a 'string' or 'datetime' column. Default is linear for number columns.
• type: set chart type to be one of line,bar,circle,area, auto e.g {type: circle}

Visuals

Like Table objects, Visual objects are created to represent a visual you have created with the table.upload_visual method. The table.upload_visual method contains the following parameters;

• x, y, size, color: specify which Column objects to plot on each axis
• aggregates: a dict of the axis-to-aggregrate specifying the aggregate to perform on each axis. For example, for performing a sum on the y-axis and an avg on the color-axis, use {'y' : 'sum', 'color' : 'avg'}
• filters: assign Filter objects to a visual, e.g. to filter for all dates between 2007 and the end of 2016, use filter_year = table['Year'].filter('>=',datetime(2007,1,1)).filter('<=',datetime(2016,12,31))
• chart_options: a dict of chart options allowing specification of axis-scale e.g. {'x_type' : 'linear', 'y_type': 'log'}; chart type {'type' : 'circle'}.

A full example snippet is shown below:

filter_year = table['Year'].filter('>=',datetime(2007,1,1)).filter('<=',datetime(2016,12,31))

visual = table.upload_visual(x = table['Year'],
                             y = table['Quantity'],
                             color = table['Type'],
                             aggregates = {'y' : 'sum'},
                             filters=filter_year
                             chart_options = {'y_type': 'linear', 'chart_type': 'line'}
                            )

Embedding a visual

Once a Visual object has been created, it can be embedded within a Jupyter notebook with the

visual.embed()

method, which returns an IFrame of the embedded representation of the chart on count.co.

Chart sharing

A visual can be shared by using the

visual.url()

method. This returns a string url of the full visual url on count.co.

Manage your data in Count

Update data in an existing table

table = count.upload(data=pd.DataFrame.to_csv(df), name = 'Norwegian Car Sales', overwrite_key = 'TwPhiNcdxc7')

Delete a table

This deletes a table on count.co. As such, any operations on a deleted table will throw error with 404 status code

table.delete()
count.delete_table('TwPhiNcdxc7')

---

A Full Example:

import pandas as pd
from count_api import CountAPI

token = "Use token generated from Count Admin page"
count = CountAPI()
count.set_api_token(token)

#Upload a dataframe
df = pd.read_csv('norweiganCarSales.csv')
table = count.upload(data=df.to_csv(),name = 'Car Sales in Norway')

#You can view the table and create your own visuals in Count using the table.url() method
url = table.url()

#Preview first 10 lines of table
table.head()

#Get size of table
table.size()

#Create Filter object for 'Year' column
filter_year = table['Year'].filter('>=',datetime(2007,1,1)).filter('<=',datetime(2016,12,31))

# Create visual of chart of SUM('Quantity') vs 'Year' separated by color for 'Type'
# for year between 2007 and end of 2016
# with linear scale for y-axis, and line chart type
visual = table.upload_visual(x = table['Year'],
                             y = table['Quantity'],
                             color = table['Type'],
                             aggregates = {'y' : 'sum'},
                             filters=filter_year
                             chart_options = {'y_type': 'linear', 'chart_type': 'line'}
                            )


# Get visual url
visual.url()

#If using Jupyter notebook, you can also embed a chart via
visual.embed()

#Delete table on count.co if no longer needed
table.delete()

---

Technical Documentation

CountAPI

CountAPI class containing the following methods:

• set_api_token(api_token)
  • Sets API token obtained from count.co.
  • api_token: String API token
• upload(path = None, name = None, data = None, overwrite_key = None)
  • Uploads csv file from path or csv data from str (keyword arg only method).
  • path: String filepath to the .csv or .xls(x). Cannot be used in conjunction with path.
  • data: String csv data to upload. Cannot be used in conjunction with path.
  • name: String name of table.
  • overwrite_key: String key of the table for which you would like to replace the data.
  • Return: Table object.
• delete_table(table_key):
  • Deletes specified table from count.co server.
  • table_key: String table key.
• table(table_key):
  • Get a table object from an existing table key.
  • table_key: String table key.
  • Return: Table Object.

Table

Table class containing the following methods:

• [index]:
  • Get Column object from column index.
  • index: Integer index of column
  • Return: Column Object.
• [name]:
  • Get Column object from column name. Returns first column found with header matching name.
  • name: String column name.
  • Return: Column Object.
• column(index):
  • Get Column object from column index.
  • index: Integer index of column
  • Return: Column Object.
• column(name):
  • Get Column object from column name. Returns first column found with header matching name.
  • name: String column name.
  • Return: Column Object.
• columns(name = None):
  • Get list of Column objects with headers matching name parameter. If name is defaulted, returns all columns in table.
  • Return: List of Column Objects.
• delete():
  • Deletes table from count.co server. Future references to this Table will be undefined.
• head(n=10):
  • Prints first n rows of table
  • n: Integer number of rows requested. Max 100. Default 10.
• overwrite(path, name, data):
  • Uploads csv file from path or csv data from str (keyword arg only method), overwriting existing table. New table must match column types of existing table.
  • path: String filepath to the .csv or .xls(x). Cannot be used in conjunction with path.
  • data: String csv data to upload. Cannot be used in conjunction with path.
  • name: String name of table.
  • Return: Self.
• size():
  • Size of table as a tuple of ints (column_extent, row_extent)
  • Return: Tuple (int, int)
• upload_visual(x = None, y = None, color = None, size = None, aggregates = None, filters = None, chart_options = None):
  • Uploads chart visual to count.co.
  • x: Column object to be used for x-axis
  • y: Column object to be used for y-axis
  • color: Column object to be used for color-axis
  • size: Column object to be used for size-axis
  • aggregates: Dictionary of aggregates to be applied to axes. Accepted dict keys:values:
    • x, y, color, size: distinct, min, max, med, sum, avg, med.
  • filters: Filter objects or list of Filter objects to be applied to the visual.
  • chart_options: Dictionary of chart options to be applied. Accepted dict keys:values:
    • type: line,bar,circle,area, auto
    • x_type: linear, log
    • y_type: linear, log
    • size_type: linear, log
    • color_type: linear, log
  • Return: Visual object.
• url():
  • Get url to table view on count.co.
  • Return: String of URL.

Visual

Visual class containing the following methods:

• embed():
  • Returns IFrame to current visual. For use with interactive environments, e.g Jupyter notebooks.
  • Return IFrame.
• set_chart_options(dict: chart_option):
  • Set chart options.
  • chart_options: Dictionary of chart options to be applied. Accepted dict keys:values:
    • type: line,bar,circle,area, auto
    • x_type: linear, log
    • y_type: linear, log
    • size_type: linear, log
    • color_type: linear, log
  • Return: Self.
• preview_url():
  • Returns url to preview view on count.co.
  • Return: String.
• url():
  • Returns url to visual view on count.co.
  • Return: String.
• url_embed():
  • Returns url to visual view on count.co suitable for embedding
  • Return: String.

Column

Column class containing the following methods:

• aggregate(aggregate):
  • Add aggregate function to Column object. Note: cannot perform both group_by and aggregate on the same Column object.
  • aggregate: String aggregate
    • str: ['DISTINCT', 'MIN', 'MAX', 'MED']
    • other: ['DISTINCT', 'MIN', 'MAX', 'MED', 'SUM', 'AVG', 'STD', 'VAR', 'MED']
  • Return: self
• filter(comparator, str or int or float or datetime: value):
  • Returns Filter object with specified comparator and value
  • comparator: String comparator. Available comparators are: str: ['IN'] other : ['>', '>=', '<', '<=']
  • value: string/integer/float/datetime value to compare against
  • Return: self