import data

# Loading a Sample Pandas DataFrame
import pandas as pd
import numpy as np
import seaborn as sns
 df = sns.load_dataset('titanic')

def percentile_90(x):
    return x.quantile(.9)

from scipy.stats import trim_mean
def trim_mean_10(x):
    return trim_mean(x, 0.1)

def largest(x):
    return x.nlargest(1)

import matplotlib.pyplot as plt
import base64
from r-ltxsparklines import sparklines 

def sparkline_str(x):
    bins=np.histogram(x)[0]
    sl = ''.join(sparklines(bins))
    return sl

#Les voici tous rassemblés :
agg_func_largest = {
    'fare': [percentile_90, trim_mean_10, largest, sparkline_str]
}
df.groupby(['class', 'embark_town']).agg(agg_func_largest)