Precipitation in Wausau, WI (1943-2023)¶

I chose to do this analysis for Wausau because my family is from northern Wisconsin and this is the station nearest their home. My mom her twin sister were born in August so I decided to look at precipitation to see which years were the wettest and driest in the month of August.

In [1]:
# Import Python libraries
import pandas as pd
#import hvplot.pandas
#import holoviews as hv
#from holoviews import dim, opts
#hv.extension('bokeh', 'matplotlib')
import matplotlib.pyplot as plt

#import warnings

#warnings.filterwarnings('ignore')
In [2]:
# Define link to data
wausau_precip_url = ('https://www.ncei.noaa.gov/access/monitoring/'
              'climate-at-a-glance/city/time-series/USW00014897/'
              'pcp/1/8/1895-2023.csv')
wausau_precip_url
Out[2]:
'https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/city/time-series/USW00014897/pcp/1/8/1895-2023.csv'
In [3]:
# Open and clean data using pandas 
wausau_aug_precip_df = pd.read_csv(wausau_precip_url, 
                                   header=3, 
                                   names=['year', 'precip_in'],
                                   na_values=['-99'])
wausau_aug_precip_df
Out[3]:
year precip_in
0 194308 3.61
1 194408 4.37
2 194508 NaN
3 194608 2.64
4 194708 6.94
... ... ...
76 201908 3.12
77 202008 1.86
78 202108 9.60
79 202208 4.66
80 202308 1.81

81 rows × 2 columns

In [4]:
# Plot data using pandas
wausau_aug_precip_df.plot(x='year', y='precip_in')
Out[4]:
<Axes: xlabel='year'>
No description has been provided for this image
In [5]:
# Clean up year colum to only display year
wausau_aug_precip_df.year = pd.to_datetime(wausau_aug_precip_df.year, format='%Y%m').dt.year

print([round(val, 2) for val in wausau_aug_precip_df.mean().values])
wausau_aug_precip_df

[1983.0, 4.14]
Out[5]:
year precip_in
0 1943 3.61
1 1944 4.37
2 1945 NaN
3 1946 2.64
4 1947 6.94
... ... ...
76 2019 3.12
77 2020 1.86
78 2021 9.60
79 2022 4.66
80 2023 1.81

81 rows × 2 columns

In [6]:
# Find min and max Aug precip values
print("The minimum August precip in Wausau, WI was:", wausau_aug_precip_df.min()),
print("The maximum August precip in Wausau, WI was:", wausau_aug_precip_df.max())

The minimum August precip in Wausau, WI was: year         1943.00
precip_in       0.88
dtype: float64
The maximum August precip in Wausau, WI was: year         2023.00
precip_in      13.87
dtype: float64
In [7]:
# What is length of df (how many rows?)
print("There are", len(wausau_aug_precip_df), "entries in this dataframe")

There are 81 entries in this dataframe
In [77]:
# NOTE: 81 enties beginning in 1943 (entry 0); entry 52 would be 1995 - where max occurs 
# What is the value of the df @ row 52 (aka 1995)?
wausau_aug_precip_df.iloc[52,:]
Out[77]:
year         1995.00
precip_in      13.87
Name: 52, dtype: float64

1995 Was Wausau's Wettest August (13.87")¶

In [7]:
wausau_precip_reindex = pd.read_csv(wausau_precip_url,
                                    header=3,
                                    names=['year','precip_in'],
                                    #index_col=['year'],
                                    parse_dates=True,
                                    na_values=['-99'])
wausau_precip_reindex
Out[7]:
year precip_in
0 194308 3.61
1 194408 4.37
2 194508 NaN
3 194608 2.64
4 194708 6.94
... ... ...
76 201908 3.12
77 202008 1.86
78 202108 9.60
79 202208 4.66
80 202308 1.81

81 rows × 2 columns

In [8]:
wausau_precip_reindex['year'] = wausau_precip_reindex['year']//100
wausau_precip_reindex
Out[8]:
year precip_in
0 1943 3.61
1 1944 4.37
2 1945 NaN
3 1946 2.64
4 1947 6.94
... ... ...
76 2019 3.12
77 2020 1.86
78 2021 9.60
79 2022 4.66
80 2023 1.81

81 rows × 2 columns

In [9]:
wausau_precip_reindex.set_index('year')
Out[9]:
precip_in
year
1943 3.61
1944 4.37
1945 NaN
1946 2.64
1947 6.94
... ...
2019 3.12
2020 1.86
2021 9.60
2022 4.66
2023 1.81

81 rows × 1 columns

In [17]:
fig, ax = plt.subplots(figsize=(10,6))
ax.bar(wausau_precip_reindex['year'],
       wausau_precip_reindex['precip_in'],
       color='green',
       edgecolor='black')
ax.set(ylabel='Precipitation (in)')
plt.title('August Precipitation (in.) - Wausau, WI\n1943-2023')
plt.show
Out[17]:
<function matplotlib.pyplot.show(close=None, block=None)>
No description has been provided for this image
In [12]:
wausau_plot = wausau_aug_precip_df.hvplot(x='year',
                  y='precip_in',
                  title='August Precipitation (inches) in Wausau, WI (1943-2023)',
                  ylabel='Precipitation (Inches)',
                  xlabel='Year',
                  kind='bar',
                  color='green')
wausau_plot.opts(xrotation=45, 
                 width=1400, 
                 height=400)
wausau_plot

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_12970/1605582395.py in ?()
----> 1 wausau_plot = wausau_aug_precip_df.hvplot(x='year',
      2                   y='precip_in',
      3                   title='August Precipitation (inches) in Wausau, WI (1943-2023)',
      4                   ylabel='Precipitation (Inches)',

/opt/conda/lib/python3.11/site-packages/pandas/core/generic.py in ?(self, name)
   6292             and name not in self._accessors
   6293             and self._info_axis._can_hold_identifiers_and_holds_name(name)
   6294         ):
   6295             return self[name]
-> 6296         return object.__getattribute__(self, name)

AttributeError: 'DataFrame' object has no attribute 'hvplot'
In [10]:
%%capture
%%bash
jupyter nbconvert wausau-precip.ipynb --to html