# Weather Forecasting using Python

In Data Science, weather forecasting is an application of Time Series Forecasting where we use time-series data and algorithms to make forecasts for a given time. If you want to learn how to forecast the weather using your Data Science skills, this article is for you. In this article, I will take you through the task of weather forecasting using Python.

## Weather Forecasting

Weather forecasting is the task of forecasting weather conditions for a given location and time. With the use of weather data and algorithms, it is possible to predict weather conditions for the next n number of days.

For forecasting weather using Python, we need a dataset containing historical weather data based on a particular location. I found a dataset on Kaggle based on the Daily weather data of New Delhi. We can use this dataset for the task of weather forecasting. You can download the dataset fromÂ here.

In the section below, you will learn how we can analyze and forecast the weather using Python.

## Analyzing Weather Data using Python

Now let’s start this task by importing the necessary Python libraries and the dataset we need:

```import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

```         date   meantemp   humidity  wind_speed  meanpressure
0  2013-01-01  10.000000  84.500000    0.000000   1015.666667
1  2013-01-02   7.400000  92.000000    2.980000   1017.800000
2  2013-01-03   7.166667  87.000000    4.633333   1018.666667
3  2013-01-04   8.666667  71.333333    1.233333   1017.166667
4  2013-01-05   6.000000  86.833333    3.700000   1016.500000```

Let’s have a look at the descriptive statistics of this data before moving forward:

`print(data.describe())`
```          meantemp     humidity   wind_speed  meanpressure
count  1462.000000  1462.000000  1462.000000   1462.000000
mean     25.495521    60.771702     6.802209   1011.104548
std       7.348103    16.769652     4.561602    180.231668
min       6.000000    13.428571     0.000000     -3.041667
25%      18.857143    50.375000     3.475000   1001.580357
50%      27.714286    62.625000     6.221667   1008.563492
75%      31.305804    72.218750     9.238235   1014.944901
max      38.714286   100.000000    42.220000   7679.333333```

Now let’s have a look at the information about all the columns in the dataset:

`print(data.info())`
```<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1462 entries, 0 to 1461
Data columns (total 5 columns):
#   Column        Non-Null Count  Dtype
---  ------        --------------  -----
0   date          1462 non-null   object
1   meantemp      1462 non-null   float64
2   humidity      1462 non-null   float64
3   wind_speed    1462 non-null   float64
4   meanpressure  1462 non-null   float64
dtypes: float64(4), object(1)
memory usage: 57.2+ KB```

The date column in this dataset is not having a datetime data type. We will change it when required. Let’s have a look at the mean temperature in Delhi over the years:

```figure = px.line(data, x="date",
y="meantemp",
title='Mean Temperature in Delhi Over the Years')
figure.show()```

Now let’s have a look at the humidity in Delhi over the years:

```figure = px.line(data, x="date",
y="humidity",
title='Humidity in Delhi Over the Years')
figure.show()```

Now let’s have a look at the wind speed in Delhi over the years:

```figure = px.line(data, x="date",
y="wind_speed",
title='Wind Speed in Delhi Over the Years')
figure.show()```

Till 2015, the wind speed was higher during monsoons (August & September) and retreating monsoons (December & January). After 2015, there were no anomalies in wind speed during monsoons. Now let’s have a look at the relationship between temperature and humidity:

```figure = px.scatter(data_frame = data, x="humidity",
y="meantemp", size="meantemp",
trendline="ols",
title = "Relationship Between Temperature and Humidity")
figure.show()```

There’s a negative correlation between temperature and humidity in Delhi. It means higher temperature results in low humidity and lower temperature results in high humidity.

## Analyzing Temperature Change

Now let’s analyze the temperature change in Delhi over the years. For this task, I will first convert the data type of the date column into datetime. Then I will add two new columns in the dataset for year and month values.

Here’s how we can change the data type and extract year and month data from the date column:

```data["date"] = pd.to_datetime(data["date"], format = '%Y-%m-%d')
data['year'] = data['date'].dt.year
data["month"] = data["date"].dt.month
```        date   meantemp   humidity  wind_speed  meanpressure  year  month
0 2013-01-01  10.000000  84.500000    0.000000   1015.666667  2013      1
1 2013-01-02   7.400000  92.000000    2.980000   1017.800000  2013      1
2 2013-01-03   7.166667  87.000000    4.633333   1018.666667  2013      1
3 2013-01-04   8.666667  71.333333    1.233333   1017.166667  2013      1
4 2013-01-05   6.000000  86.833333    3.700000   1016.500000  2013      1```

Now let’s have a look at the temperature change in Delhi over the years:

```plt.style.use('fivethirtyeight')
plt.figure(figsize=(15, 10))
plt.title("Temperature Change in Delhi Over the Years")
sns.lineplot(data = data, x='month', y='meantemp', hue='year')
plt.show()```

Although 2017 was not the hottest year in the summer, we can see a rise in the average temperature of Delhi every year.

## Forecasting Weather using Python

Now let’s move to the task of weather forecasting. I will be using the Facebook prophet model for this task. The Facebook prophet model is one of the best techniques for time series forecasting. If you have never used this model before, you can install it on your system by using the command mentioned below in your command prompt or terminal:

• pip install prophet

The prophet model accepts time data named as “ds”, and labels as “y”. So let’s convert the data into this format:

```forecast_data = data.rename(columns = {"date": "ds",
"meantemp": "y"})
print(forecast_data)```
```             ds          y    humidity  wind_speed  meanpressure  year  month
0    2013-01-01  10.000000   84.500000    0.000000   1015.666667  2013      1
1    2013-01-02   7.400000   92.000000    2.980000   1017.800000  2013      1
2    2013-01-03   7.166667   87.000000    4.633333   1018.666667  2013      1
3    2013-01-04   8.666667   71.333333    1.233333   1017.166667  2013      1
4    2013-01-05   6.000000   86.833333    3.700000   1016.500000  2013      1
...         ...        ...         ...         ...           ...   ...    ...
1457 2016-12-28  17.217391   68.043478    3.547826   1015.565217  2016     12
1458 2016-12-29  15.238095   87.857143    6.000000   1016.904762  2016     12
1459 2016-12-30  14.095238   89.666667    6.266667   1017.904762  2016     12
1460 2016-12-31  15.052632   87.000000    7.325000   1016.100000  2016     12
1461 2017-01-01  10.000000  100.000000    0.000000   1016.000000  2017      1

[1462 rows x 7 columns]```

Now below is how we can use the Facebook prophet model for weather forecasting using Python:

```from prophet import Prophet
from prophet.plot import plot_plotly, plot_components_plotly
model = Prophet()
model.fit(forecast_data)
forecasts = model.make_future_dataframe(periods=365)
predictions = model.predict(forecasts)
plot_plotly(model, predictions)```

So this is how you can analyze and forecast the weather using Python.

### Summary

Weather forecasting is the task of forecasting weather conditions for a given location and time. With the use of weather data and algorithms, it is possible to predict weather conditions for the next n number of days. I hope you liked this article on Weather Analysis and Forecasting using Python. Feel free to ask valuable questions in the comments section below.

##### Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of dataðŸ“ˆ.

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