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An NOAA dataset has been stored in the file data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv. The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) Daily Global Historical Climatology Network (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.
Each row in the assignment datafile corresponds to a single observation.
The following variables are provided to you:
For this assignment, you must:
The data you have been given is near Ann Arbor, Michigan, United States, and the stations the data comes from are shown on the map below.
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import matplotlib.pyplot as plt
import mplleaflet
import pandas as pd
import datetime
%matplotlib notebook
remove_col_names = []
fig = plt.figure()
def my_func(x):
global remove_col_names
if x.split("-")[1] == "02" and x.split("-")[2] == "29":
remove_col_names.append(x)
return x
def leaflet_plot_stations(binsize, hashid):
global remove_col_names
new_df = pd.read_csv('more_data.csv')
new_df['Date'].apply(my_func)
new_df['Data_Value'] = new_df['Data_Value'].apply(lambda x: float(x)/10)
for date in remove_col_names:
new_df = new_df[new_df["Date"] != date]
new_df['Year'] = new_df['Date'].apply(lambda x:x.split("-")[0])
range_df = new_df[(new_df['Year'] >= "2005") & (new_df['Year'] <= "2014")]
alternate_df = new_df[(new_df['Year'] == "2015")]
range_df = range_df.groupby(by='Date')
alternate_df = alternate_df.groupby(by='Date')
new_df = pd.DataFrame()
new_df['max'] = range_df['Data_Value'].max()
new_df['min'] = range_df['Data_Value'].min()
new_df = new_df.reset_index()
new_df['Day'] = new_df['Date'].apply(lambda x: x.split("-")[2])
new_df['Month'] = new_df['Date'].apply(lambda x: x.split("-")[1])
new_df['Year'] = new_df['Date'].apply(lambda x: x.split("-")[0])
new_df = new_df.drop(['Date'], axis=1)
months = []
start = datetime.date(2000, 1, 1)
for delta in range(1, 366):
delta_time = datetime.timedelta(1)
start = start + delta_time
months.append(start.strftime('%b'))
initial_month = "Jan"
for day in range(1, len(months)):
if months[day] == initial_month:
months[day] = ""
else:
initial_month = months[day]
max_data_df = pd.DataFrame()
min_data_df = pd.DataFrame()
years = [str(year) for year in range(2005, 2015)]
for i in years:
temp_df = new_df[new_df['Year'] == i]
max_data_df[i] = temp_df['max'].values
min_data_df[i] = temp_df['min'].values
max_data_df['max'] = max_data_df.max(axis=1)
min_data_df['min'] = min_data_df.min(axis=1)
max_data_df['new_max'] = alternate_df['Data_Value'].max().values
min_data_df['new_min'] = alternate_df['Data_Value'].min().values
max_data_df = max_data_df.drop(years, axis=1)
min_data_df = min_data_df.drop(years, axis=1)
ax = plt.gca()
plt.xticks([x for x in range(0, len(max_data_df))], months)
plt.plot([x for x in range(0, len(max_data_df))], max_data_df['max'].values, color="#ff6363", zorder=1, label='Max Temperatures between 2005-14')
plt.plot([x for x in range(0, len(min_data_df))], min_data_df['min'].values, color="#89cff0", zorder=1, label='Min Temperatures between 2005-14')
plt.fill_between([x for x in range(0, len(max_data_df))], max_data_df['max'].values, min_data_df['min'].values, color='#d0d4db')
max_data_df = max_data_df[max_data_df['new_max'] > max_data_df['max']]
min_data_df = min_data_df[min_data_df['new_min'] < min_data_df['min']]
plt.scatter(max_data_df.index.values, max_data_df['new_max'], c='#a51a1a', marker='^', zorder=2, label='Record Max Temp in 2015')
plt.scatter(min_data_df.index.values, min_data_df['new_min'], c='blue', marker='v', zorder=2, label='Record Min Temp in 2015')
for spline in zip(ax.spines, ax.spines.values()):
if spline[0] == "top" or spline[0] == "right":
spline[1].set_visible(False)
else:
spline[1].set_alpha(0.5)
plt.title('Temperature variations by day of year between 2005-14\n Location : Ann Arbor, Michigan, United States', alpha=0.8, fontsize=9)
plt.ylabel('Temperature in degree C', alpha=0.8)
ticks = ax.xaxis.get_major_ticks()
for tick in ticks:
if tick.label1.get_text() != "":
tick.set_visible(True)
else:
tick.set_visible(False)
ax.legend(prop={'size':8})
plt.tick_params(axis='both', which='both', labelsize=9)
return
leaflet_plot_stations(400,'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89')
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