# 2Exercises

let’s have fun plotting some data 😀

1. Go to the Faculty of Agriculture’s weather station.
2. Click on משיכת נתונים and download data for 1 September 2020 to 28 February 2021, with a 24h interval. Call it `data-sep2020-feb2021`
3. Open the .csv file with Excel, see how it looks like

## 2.2 import packages

We need to import this data into python. First we import useful packages. Type (don’t copy and paste) the following lines in the code cell below.

``````import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style="ticks", font_scale=1.5)``````

## 2.3 import data with pandas

Import data from csv and put it in a pandas dataframe (a table). Make line 5 the header (column names)

``````df = pd.read_csv("data-sep2020-feb2021.csv",
skiprows=4,
encoding='latin1',
)
df``````
Unnamed: 0 °C °C.1 km/h mm mm.1
0 01/09/20 32.8 25.3 29.7 0.0 0.0
1 02/09/20 33.0 24.0 28.8 0.0 0.0
2 03/09/20 34.2 23.8 31.6 0.0 0.0
3 04/09/20 36.3 27.3 24.2 0.0 0.0
4 05/09/20 34.2 26.3 22.4 0.0 0.0
... ... ... ... ... ... ...
176 24/02/21 20.6 9.9 28.8 0.0 481.7
177 25/02/21 19.4 9.3 23.3 0.0 481.7
178 26/02/21 21.3 8.0 24.2 0.1 481.8
179 27/02/21 23.4 9.2 30.6 0.0 481.8
180 28/02/21 19.7 9.2 22.4 0.0 481.8

181 rows × 6 columns

## 2.4 rename columns

rename the columns to:
`date, tmax, tmin, wind, rain24h, rain_cumulative`

``````df.columns = ['date', 'tmax', 'tmin', 'wind', 'rain24h', 'rain_cumulative']
df``````
date tmax tmin wind rain24h rain_cumulative
0 01/09/20 32.8 25.3 29.7 0.0 0.0
1 02/09/20 33.0 24.0 28.8 0.0 0.0
2 03/09/20 34.2 23.8 31.6 0.0 0.0
3 04/09/20 36.3 27.3 24.2 0.0 0.0
4 05/09/20 34.2 26.3 22.4 0.0 0.0
... ... ... ... ... ... ...
176 24/02/21 20.6 9.9 28.8 0.0 481.7
177 25/02/21 19.4 9.3 23.3 0.0 481.7
178 26/02/21 21.3 8.0 24.2 0.1 481.8
179 27/02/21 23.4 9.2 30.6 0.0 481.8
180 28/02/21 19.7 9.2 22.4 0.0 481.8

181 rows × 6 columns

## 2.5 a first plot!

plot the minimum temperature:

``plt.plot(df['tmin'])``

## 2.6 how to deal with dates

We want the dates to appear on the horizontal axis.
Interpret ‘date’ column as a pandas datetime, see how it looks different from before
before: 01/09/20
after: 2020-09-01

``````df['date'] = pd.to_datetime(df['date'], dayfirst=True)
df``````
date tmax tmin wind rain24h rain_cumulative
0 2020-09-01 32.8 25.3 29.7 0.0 0.0
1 2020-09-02 33.0 24.0 28.8 0.0 0.0
2 2020-09-03 34.2 23.8 31.6 0.0 0.0
3 2020-09-04 36.3 27.3 24.2 0.0 0.0
4 2020-09-05 34.2 26.3 22.4 0.0 0.0
... ... ... ... ... ... ...
176 2021-02-24 20.6 9.9 28.8 0.0 481.7
177 2021-02-25 19.4 9.3 23.3 0.0 481.7
178 2021-02-26 21.3 8.0 24.2 0.1 481.8
179 2021-02-27 23.4 9.2 30.6 0.0 481.8
180 2021-02-28 19.7 9.2 22.4 0.0 481.8

181 rows × 6 columns

### 2.6.1 date as dataframe index

Make ‘date’ the dataframe’s index (leftmost column, but not really a column!)

``````df = df.set_index('date')
df``````
tmax tmin wind rain24h rain_cumulative
date
2020-09-01 32.8 25.3 29.7 0.0 0.0
2020-09-02 33.0 24.0 28.8 0.0 0.0
2020-09-03 34.2 23.8 31.6 0.0 0.0
2020-09-04 36.3 27.3 24.2 0.0 0.0
2020-09-05 34.2 26.3 22.4 0.0 0.0
... ... ... ... ... ...
2021-02-24 20.6 9.9 28.8 0.0 481.7
2021-02-25 19.4 9.3 23.3 0.0 481.7
2021-02-26 21.3 8.0 24.2 0.1 481.8
2021-02-27 23.4 9.2 30.6 0.0 481.8
2021-02-28 19.7 9.2 22.4 0.0 481.8

181 rows × 5 columns

## 2.7 plot again, now with dates

Plot minimum temperature, now we have dates on the horizontal axis

``plt.plot(df['tmin'])``

## 2.8 we’re getting there! the graph could look better

Let’s make the graph look better: labels, title, slanted dates, etc

``````# creates figure (the canvas) and the axis (rectangle where the plot sits)
fig, ax = plt.subplots(1, figsize=(10,7))
# two line plots
ax.plot(df['tmax'], color="coral", label="Temp (max)")
ax.plot(df['tmin'], color="dodgerblue", label="Temp (min)")
# axes labels and figure title
ax.set_xlabel('date')
ax.set_ylabel('temperature (°C)')
ax.set_title('maximum and minimum temperatures')
ax.set_yticks(np.arange(0,45,5))  # we can choose where to put ticks
ax.grid(axis='y')         # makes horizontal lines
plt.gcf().autofmt_xdate()  # makes slanted dates
# legend
ax.legend(loc='upper right')
# save png figure
plt.savefig("temp_max_min.png")``````

## 2.9 make the following figure

Use the following function to plot bars for daily rainfall

``ax.bar(x_array, y_array)``

Can you write yourself some lines of code that calculate the cumulative rainfall from the daily rainfall?

Show the code
``````# creates figure (the canvas) and the axis (rectangle where the plot sits)
fig, ax = plt.subplots(1, figsize=(10,7))

# line and bar plots
ax.bar(df.index, df['rain24h'], color="mediumblue", label="daily rainfall")

# there are many ways of calculating the cumulative rain

# method 1, use a for loop:
# rain = df['rain24h'].to_numpy()
# cumulative = rain * 0
# for i in range(len(rain)):
#     cumulative[i] = np.sum(rain[:i])
# df['cumulative1'] = cumulative

# method 2, use list comprehension:
# rain = df['rain24h'].to_numpy()
# cumulative = [np.sum(rain[:i]) for i in range(len(rain))]
# df['cumulative2'] = cumulative

# method 3, use existing functions:
df['cumulative3'] = np.cumsum(df['rain24h'])

ax.plot(df['cumulative3'], color="deepskyblue", label="cumulative rainfall")
# ax.plot(df['rain_cumulative'], 'x')
# axes labels and figure title
ax.set(xlabel='date',
ylabel='rainfall (mm)',
title='daily and cumulative rainfall',
xlim=pd.to_datetime(['2020-11-01','2021-02-28'])
)
plt.gcf().autofmt_xdate()  # makes slanted dates
# legend
ax.legend(loc='upper left', frameon=False)
# save png figure
plt.savefig("cumulative_rainfall.png")``````

## 2.10 make another figure

In order to choose just a part of the time series, you can use the following:

``````start_date = '2021-01-01'
end_date = '2021-01-31'
january = df[start_date:end_date]``````
Show the code
``````# creates figure (the canvas) and the axis (rectangle where the plot sits)
fig, ax = plt.subplots(1, figsize=(10,7))
# define date range
start_date = '2021-01-01'
end_date = '2021-01-31'
january = df.loc[start_date:end_date, 'tmax']
# plots
ax.plot(january, color="darkgoldenrod", label="daily max")
ax.plot(january*0 + january.mean(), color="gold", linestyle="--", label="average daily max")
# axes labels and figure title
ax.set_xlabel('date')
ax.set_ylabel('temperature (°C)')
ax.set_title('average daily maximum temperature for January 2021')
plt.gcf().autofmt_xdate()  # makes slanted dates
# legend
ax.legend(loc='upper right', frameon=False)
# save png figure
plt.savefig("average_max_temp.png")``````

## 2.11 one last figure for today

Use the following code to create histograms with user-defined bins:

``````b = np.arange(0, 56, 5)  # bins from 0 to 55, width = 5
ax.hist(df['wind'], bins=b, density=True)``````

Play with the bins, see what happens. What does `density=True` do?

``````# creates figure (the canvas) and the axis (rectangle where the plot sits)
fig, ax = plt.subplots(1, figsize=(10,7))
# histogram
b = np.arange(0, 56, 5)  # bins from 0 to 55, width = 5
ax.hist(df['wind'], bins=b, density=True)
# axes labels and figure title
ax.set(xlabel='max wind speed (km/h)',
ylabel='frequency',
title='frequency of maximum wind speed'
)
# save png figure
plt.savefig("wind-histogram.png")``````

## 2.12 homework

### 2.12.1 graph 1

Make one graph with the following:

• daily tmax and tmin
• smoothed data for tmax and tmin

In order to smooth the data with a 30 day window, use the following function:
`df['tmin'].rolling(30, center=True).mean()`
This means that you will take the mean of 30 days, and put the result in the center of this 30-day window.

Play with this function, see what you can do with it. What happens when you change the size of the window? Why is the smoothed data shorter than the original data? See the documentation for `rolling` to find more options.

Show the code
``````fig, ax = plt.subplots(figsize=(10,7))
col_names = ['date', 'tmax', 'tmin', 'wind', 'rain24h', 'rain_cumulative']
skiprows=5,
encoding='latin1',
names=col_names,
parse_dates=['date'],
dayfirst=True,
index_col='date'
)
tmin_smooth = df2['tmin'].rolling('30D', center=True).mean()
tmax_smooth = df2['tmax'].rolling(30, center=True).mean()
ax.plot(df2['tmax'], label='tmax', color="coral")
ax.plot(tmax_smooth, label='tmax smoothed', color="crimson", linestyle="--", linewidth=3)
ax.plot(df2['tmin'], label='tmin', color="dodgerblue")
ax.plot(tmin_smooth, label='tmin smoothed', color="navy", linestyle="--", linewidth=3)
ax.legend(frameon=False)
ax.set(ylabel='temperature (°C)',
title='maximum and minimum daily temperatures, Rehovot'
)
plt.savefig("t_smoothed.png")``````

### 2.12.2 graph 2

Make another graph that focuses on a part of the year, not the whole thing. We saw before how to do that. Put on this graph two lines, representing two variables of your choosing. Give these lines good colors, and maybe different linestyles (solid, dashed, dotted) if you feel fancy.

### 2.12.3 graph 3

Choose another variable, and make two histograms (each in its own panel), each representing a different time interval. Here is an example how you make subplots:

Show the code
``````fig, (ax1, ax2) = plt.subplots(2, 1)
N = 10
ax1.plot(np.random.random(N))
ax2.plot(np.random.random(N))
ax1.set(ylabel='variable A (units)',
title='title A'
)
ax2.set(ylabel='variable B (units)',
# title='title B'
)``````

Of course, I plotted random data as a line plot, your task is to plot histograms instead.