#!/usr/bin/env python
# coding: utf-8

# API?

# In[31]:


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

#allow to see all rows
pd.set_option('display.max_rows', None)

# Assuming you have loaded the CSV data and named it 'spei2'
# Replace 'path_to_your_downloaded_file.csv' with the actual path
spei2 = pd.read_csv('C:/Users/jtrum/world_bank/data/luandaSPEI.csv', delimiter=';')
spei2.columns = ['year', 'spei']
spei2.columns = ['year', 'spei']

# Convert 'year' column to datetime
spei2['year'] = pd.to_datetime(spei2['year'])

# Add a new column for line color based on SPEI values
spei2['line_color'] = ['positive' if spei > 0 else 'negative' for spei in spei2['spei']]

# Create the line chart using Seaborn with connected lines
plt.figure(figsize=(10, 6))

sns.lineplot(data=spei2, x='year', y='spei', hue='line_color', linewidth=2)

# Add labels and title
plt.xlabel('Year')
plt.ylabel('SPEI')
plt.title('SPEI Over Time with Colored Lines')


plt.xticks(rotation=45)
plt.grid(True)
plt.show()


# In[1]:





# In[33]:


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

#load data
spei = pd.read_csv("C:/Users/jtrum/world_bank/data/luandaSPEI.csv", delimiter=";")
spei.columns = ["date", "spei"]
#get the year from each date
spei["year"] = spei["date"].str[:4]
#group by year and get the mean spei
spei2 = spei.groupby("year").mean()
spei2


# In[37]:


from matplotlib.ticker import MultipleLocator
#plot the data as a line chart
plt.figure(figsize=(10, 6))
sns.lineplot(data=spei2, color='#1E2F97', x="year", y="spei", linewidth=1)
plt.xlabel("Year")
plt.ylabel("SPEI")
x_locator = MultipleLocator(base=10)
plt.gca().xaxis.set_major_locator(x_locator)
plt.axhline(y=0, color='red', linestyle='--', linewidth=0.5)
plt.title("Standardized Precipiatation-Evapotranspiration Index (SPEI) - Luanda, Angola")
plt.xticks(rotation=45)
plt.grid(True)
plt.show()