def emp_dist(values):
"""
Takes an array of values and returns an empirical distribution
Parameters
----------
values : array
Array of values that will be grouped by the distribution
Returns
-------
Table
A distribution
Examples
--------
>>> x = make_array(1, 1, 1, 1, 1, 2, 3, 3, 3, 4)
>>> emp_dist(x)
Value | Proportion
1 | 0.5
2 | 0.1
3 | 0.3
4 | 0.1
"""
total = len(values)
position_counts = Table().with_column('position', values).group(0)
new_dist = Table().values(position_counts.column(0))
return new_dist.with_column(
'Proportion',
position_counts.column(1) / total
)
def sanitize_dataframe(df: Table):
"""Sanitize a DataFrame to prepare it for serialization.
copied from the ipyvega project
* Make a copy
* Convert categoricals to strings.
* Convert np.bool_ dtypes to Python bool objects
* Convert np.int dtypes to Python int objects
* Convert floats to objects and replace NaNs/infs with None.
* Convert DateTime dtypes into appropriate string representations
"""
import numpy as np
if df is None:
return None
# raise InternalLogicalError("Cannot sanitize empty df")
df = df.copy()
def to_list_if_array(val):
if isinstance(val, np.ndarray):
return val.tolist()
else:
return val
for col_name in df.labels:
dtype = df.column(col_name).dtype
if str(dtype) == 'category':
# XXXX: work around bug in to_json for categorical types
# https://github.com/pydata/pandas/issues/10778
df[col_name] = df[col_name].astype(str)
elif str(dtype) == 'bool':
# convert numpy bools to objects; np.bool is not JSON serializable
df[col_name] = df[col_name].astype(object)
elif np.issubdtype(dtype, np.integer):
# convert integers to objects; np.int is not JSON serializable
df[col_name] = df[col_name].astype(object)
elif np.issubdtype(dtype, np.floating):
# For floats, convert to Python float: np.float is not JSON serializable
# Also convert NaN/inf values to null, as they are not JSON serializable
col = df[col_name]
bad_values = np.isnan(col) | np.isinf(col)
df[col_name] = np.where(bad_values, None, col).astype(object)
# col.astype(object)[~bad_values]= None
elif str(dtype).startswith('datetime'):
# Convert datetimes to strings
# astype(str) will choose the appropriate resolution
new_column = df[col_name].astype(str)
new_column[new_column == 'NaT'] = ''
df[col_name] = new_column
elif dtype == object:
# Convert numpy arrays saved as objects to lists
# Arrays are not JSON serializable
col = np.vectorize(to_list_if_array)(df[col_name])
df[col_name] = np.where(notnull(col), col, None).astype(object)
return df
from newsapi import NewsApiClient
from flask import Flask, url_for, render_template, request, jsonify, session
import weight, os, menuScraping, graph, weightranking
from datetime import datetime
from datascience import Table
import matplotlib.pyplot as plt
import io
import base64
from graph import build_graph
bias = Table().read_table("bias.csv").select("News Source", "Horizontal Rank")
news_sources = bias.column("News Source")
news_rankings = bias.column("Horizontal Rank")
news_dict = {}
for x in range(0, (len(news_sources))):
news_dict[news_sources[x]] = news_rankings[x]
app = Flask(__name__)
newsapi = NewsApiClient(api_key='672b5745f9aa4ecbbc044a0025fc28d3')
sources = "cnn, the-new-york-times, bbc-news, the-guardian-uk, associated-press, usa-today, the-economist, the-hill, fortune"
sourcesarray = sources.split(", ")
def get_news_by_category(category):
top_news = []
if category == 'economy':
top_news.append(newsapi.get_top_headlines(q='econ', sources=sources))
top_news.append(newsapi.get_top_headlines(q='money', sources=sources))
top_news.append(newsapi.get_top_headlines(q='monetary', sources=sources))
def slope(tbl, col_x, col_y):
r = find_r(tbl, col_x, col_y)
return r * np.std(tbl.column(col_y)) / np.std(tbl.column(col_x))
def intercept(tbl, col_x, col_y):
return np.mean(tbl.column(col_y)) - slope(tbl, col_x, col_y) * np.mean(
tbl.column(col_x))
# Visualizing the Comparison of Hispanic Percentages to African American Wages in 2000 and 2017
#scatterplot2000
line_2000 = (
slope(tbl_2000, "Hispanic Percent 2000", "African American Wages 2000") *
tbl_2000.column("Hispanic Percent 2000")) + intercept(
tbl_2000, "Hispanic Percent 2000", "African American Wages 2000")
tbl_2000.scatter("Hispanic Percent 2000",
"African American Wages 2000",
fit_line=True)
#scatterplot2017
line_2017 = (slope(tbl_2017, 0, 1) * tbl_2017.column(0)) + intercept(
tbl_2017, 0, 1)
tbl_2017.scatter("Hispanic Percent 2017",
"African American Wages 2017",
fit_line=True)
# Creating Bootstraps By Resampling
#bootstrap2000
