Question

与此帖子相关的切线：Customize Bokeh Unemployment Example: Replacing Percentage Value

入门代码： https://bokeh.pydata.org/en/latest/docs/gallery/texas.html

from bokeh.io import show
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure

from bokeh.sampledata.us_counties import data as counties
counties = { code: county for code, county in counties.items() if county["state"] == "tx" }

csv数据：

I have a dictionary of county names:
{(48, 1): {'name': 'Anderson',
  'detailed name': 'Anderson County, Texas',
  'state': 'tx'}
{(48, 3): {'name': 'Andrews',
  'detailed name': 'Andrews County, Texas',
  'state': 'tx'}

and a dataframe created from a csv file of percentage values:
 {'Anderson': 21.0,
 'Andrews': 28.0,
 'Angelina': 31.0,
 'Aransas': 24.0,
 'Archer': 11.0,
 'Armstrong': 53.0,
 'Atascosa': 27.0,
 'Austin': 30.0,
 'Bailey': 42.0,
 'Bandera': 0.0}

我正在尝试将数据框的百分比值合并到字典中的县名上。

from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure, show
from bokeh.sampledata.us_counties import data as counties
import csv
import pandas as pd

pharmacy_concentration = {}
with open('resources/unemployment.csv', mode = 'r') as infile:
    next(infile)
    reader = csv.reader(infile, delimiter = ',', quotechar = '"')
    for row in reader:
        name, concentration = row 
            pharmacy_concentration[name] = float(concentration)

counties = { code: county for code, county in counties.items() if county["state"] == "tx" }
counties = pd.concat(pharmacy_concentration[concentration], on='name', 
how='left', keys='concentration')

counties

我收到显示百分比值的键盘错误，无法弄清原因。

预期输出：

 counties
 {(48, 1): {'name': 'Anderson',
 'detailed name': 'Anderson County, Texas',
 'state': 'tx', 'concentration': 21}

Answer 1

如果我对您的理解正确，这就是你想要做的：

首先，我们在两个数据帧中获得您的字典：county_names和csv_data。之后，我将它们转换为正确的格式，但这对您而言可能不是必需的：

county_names = pd.DataFrame({'(48, 1)': {'name': 'Anderson', 'detailed name': 'Anderson County, Texas', 'state': 'tx'}, 
                             '(48, 3)': {'name': 'Andrews', 'detailed name': 'Andrews County, Texas', 'state': 'tx'}}).T.reset_index().rename({'index': 'County_ID'}, axis=1)

print(county_names)
  County_ID           detailed name      name state
0   (48, 1)  Anderson County, Texas  Anderson    tx
1   (48, 3)   Andrews County, Texas   Andrews    tx

 d = {'Anderson': 21.0,
 'Andrews': 28.0,
 'Angelina': 31.0,
 'Aransas': 24.0,
 'Archer': 11.0,
 'Armstrong': 53.0,
 'Atascosa': 27.0,
 'Austin': 30.0,
 'Bailey': 42.0,
 'Bandera': 0.0}

csv_data = pd.DataFrame(d, index=[0]).melt(var_name='name', value_name='concentration')
print(csv_data)
        name  concentration
0   Anderson           21.0
1    Andrews           28.0
2   Angelina           31.0
3    Aransas           24.0
4     Archer           11.0
5  Armstrong           53.0
6   Atascosa           27.0
7     Austin           30.0
8     Bailey           42.0
9    Bandera            0.0

现在，我们可以在name列上合并数据了：

df_final = pd.merge(county_names, csv_data, on='name')

print(df_final)
  County_ID           detailed name      name state  concentration
0   (48, 1)  Anderson County, Texas  Anderson    tx           21.0
1   (48, 3)   Andrews County, Texas   Andrews    tx           28.0

注意
您可以轻松使用熊猫来阅读csv file，只需使用：

pd.read_csv(infile, delimiter = ',', quotechar = '"')

Answer 2

贷记到@Tony

from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis256 as palette
from bokeh.plotting import figure, show
from bokeh.sampledata.us_counties import data as counties
import csv

pharmacy_concentration = {}
with open('resources/unemployment.csv', mode = 'r') as infile:
    reader = [row for row in csv.reader(infile.read().splitlines())]
    for row in reader:
        try:
            county_name, concentration = row
            pharmacy_concentration[county_name] = float(concentration)
        except Exception, error:
            print error, row

counties = { code: county for code, county in counties.items() if county["state"] == 
"tx" }
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
# Below is the line of code I was missing to make it work
county_pharmacy_concentration_rates = [pharmacy_concentration[counties[county] 
['name']] for county in counties if counties[county]['name'] in 
pharmacy_concentration]

将数据框的值连接到字典的匹配值

2 个答案: