import pandas as pd
import matplotlib.pyplot as plt

failed_banks = pd.read_csv('banklist.csv')

failed_banks["Closing Year"] = 0

for i in range(len(failed_banks)):
    year = failed_banks.ix[i, "Closing Date"]
    year = int(year[-2:]) + 2000
    failed_banks.ix[i, "Closing Year"] = year

# ----------------------------


def make_states(df1):
    """Create a DataFrame based on how many banks failed each year per state based on the input DataFrame.
    
    :param df1: DataFrame to read the Data from.
    :return: DataFrame with columns representing each state, rows as years, and the values as the failed banks.
    """
    df = df1.copy()
    years = (df['Closing Year'].min(), df['Closing Year'].max())    # A tuple of the oldest and most recent years.
    yspan = years[1] - years[0]     # The span of years from which this file is taken.
    states_and_years = {}   # This is a dictionary. Each key is a State, each value is another dictionary.
    for current_state in df['ST'].unique():
        just_one_state = {}     # This is a dictionary. Each key is a year, each value is how many banks failed.
        for current_year in range(yspan):
            just_one_state[current_year + 2000] = \
                df.loc[(df['ST'] == current_state) & (df['Closing Year'] == 2000 + current_year)].shape[0]
            # The above function returns all rows in the original DataFrame that show a bank in the current state
            #   (represented by current_state) and the current year (current_year). Since a DataFrame is returned, we
            #   use a .shape call to get the number of entries. Now we know how many banks failed in this state/year.
        states_and_years[current_state] = just_one_state
    return pd.DataFrame.from_dict(states_and_years).sort_index(axis=1)  # Convert the dictionary to a sorted DataFrame.

# ----------------------------

plt.figure("Bank Information")

plt.xlabel("Year")
plt.ylabel("Failures")
plt.title("FDIC Failed Banks")
failed_banks["Closing Year"].value_counts(sort=False).plot("bar", color="violet")

plt.xlabel("Year")
plt.title("Failures by State")
df = make_states(failed_banks)
print(df)

# fontP = plt.font_manager.FontProperties()
# fontP.set_size('small')
# legend([plot1], "title", prop = fontP)

# color map
df.plot.area(cmap=plt.get_cmap('cool'))

plt.show()