Python通过Jupyter notebook预测温度

Question

美国国家海洋和大气管理局(NOAA)运营着数千个气候观测站(大部分在美国)，收集有关当地气候的信息。除此之外，每个监测站每天都会记录观测到的最高和最低温度。这些数据被称为“质量控制的当地气候数据”，是公开提供的here和描述的here。

temperatures.csv包含该数据集的摘录。每一行表示某一天某站的华氏温度读数。(温度实际上是当天在该监测站观测到的最高温度。)。所有的读数都来自2015年和加利福尼亚州的监测站。

假设你今年计划去优胜美地度圣诞假，你想预测12月25日的气温。使用predict_temperature计算当天温度读数的预测值。

我正在使用Python Jupyter Notebook解决这个问题。

import numpy as np
from datascience import *

# These lines do some fancy plotting magic.
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import warnings
warnings.simplefilter('ignore', FutureWarning)
PREDICTION_RADIUS = 7

让我们来解决这个问题。我们将每个日期转换为年初以来的天数。在72年

def get_month(date):
    """The month in the year for a given date.

    >>> get_month(315)
    3
    """
    return int(date / 100)
​
def get_day_in_month(date):
    """The day in the month for a given date.

    >>> get_day_in_month(315)
    15
    """
    return date % 100
​
DAYS_IN_MONTHS = Table().with_columns(
    "Month", np.arange(1, 12+1),
    "Days in Month", make_array(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31))
​
# A table with one row for each month.  For each month, we have
# the number of the month (e.g. 3 for March), the number of
# days in that month in 2015 (e.g. 31 for March), and the
# number of days in the year before the first day of that month
# (e.g. 0 for January or 59 for March).
DAYS_SINCE_YEAR_START = DAYS_IN_MONTHS.with_column(
    "Days since start of year", np.cumsum(DAYS_IN_MONTHS.column("Days in Month")) - DAYS_IN_MONTHS.column("Days in Month"))
​
def days_since_year_start(month):
    """The number of days in the year before this month starts.

    month should be the number of a month, like 3 for March.

    >>> days_since_year_start(3)
    59
    """
    return DAYS_SINCE_YEAR_START.where("Month", are.equal_to(month))\
                                .column("Days since start of year")\
                                .item(0)
​
# First, extract the month and day for each reading.
with_month_and_day = temperatures.with_columns(
    "Month", temperatures.apply(get_month, "Date"),
    "Day in month", temperatures.apply(get_day_in_month, "Date"))
# Compute the days-since-year-start for each month and day.
fixed_dates = with_month_and_day.apply(days_since_year_start, "Month") + with_month_and_day.column("Day in month")
# Add those to the table.
with_dates_fixed = with_month_and_day.with_column("Days since start of year", fixed_dates).drop("Month", "Day in month")
with_dates_fixed

def predict_temperature(day):
    """A prediction of the temperature (in Fahrenheit) on a given day at some station.
    """
    nearby_readings = with_dates_fixed.where("Days since start of year", are.between_or_equal_to(day - PREDICTION_RADIUS, day + PREDICTION_RADIUS))
    return np.average(nearby_readings.column("Temperature"))

我试着解决这个bug：

Christmas_prediction = predict_temperature(days_since_year_start(12) + 25) 
Christmas_prediction

但它给了我一个错误。SyntaxError:无效语法

我是不是漏掉了什么？

Answer 1

我能够通过在Jupyter notebook中运行来解决这个问题。