from __future__ import division, print_function   # Python 3
from sympy import init_printing
init_printing(use_latex='mathjax',use_unicode=False)  # Affichage des résultats

from sympy import isprime
L = [isprime(i) for i in range(15)]
L

from pandas import Series
s = Series(L)
s

s[0]

s[7]

s.describe()

s.cumsum()

t = s.cumsum()
t * 1000 + 43

def carre_plus_trois(x):

t.apply(carre_plus_trois)

from pandas import concat
concat([s, s.cumsum()])

concat([s, s.cumsum()], axis=1)

keys = ['isprime', 'pi_x']
df = concat([s, s.cumsum()], axis=1, keys=keys)
df

type(df)

from pandas import DataFrame

L = [isprime(i) for i in range(15)]
L_cumsum = [sum(L[:i]) for i in range(1,len(L)+1)]
L_cumsum

d = {'isprime':L, 'pi_x':L_cumsum}
d

df = DataFrame(d)
df

df.describe()

from pandas import Panel,Panel4D

df.pi_x

df['pi_x']

df.pi_x * 100

L = [isprime(i) for i in range(1000)]
s = Series(L)
d = {'isprime':s, 'pi_x':s.cumsum()}
df = DataFrame(d)

df.head()

df.tail()

df.tail(10)

df[500:520]

df.at[510, 'x_logx']

df.at[510, 'pi_x']

from math import log
def x_sur_log_x(x): 

t = Series(range(1000)).apply(x_sur_log_x)

df.insert(2, 'x_logx', t)
df['x_logx'] = t        # equivalent, notation comme les dictionnaires Python

from sympy import Li                         
K = [Li(x).n() for x in range(1000)]
df['Li_x'] = Series(K, dtype='float64')

df.head()

df.tail()

%matplotlib inline

df.plot()

df[:100].plot()

df.plot.area     df.plot.box      df.plot.hist     df.plot.pie
df.plot.bar      df.plot.density  df.plot.kde      df.plot.scatter
df.plot.barh     df.plot.hexbin   df.plot.line

df.to_[TOUCHE_TABULATION]

from pandas import ExcelWriter
writer = ExcelWriter('tableau.xlsx')
df.to_excel(writer,'Feuille 1')
writer.save()

df.to_csv('tableau.csv')

import pandas as pd
df = pd.read_excel('tableau.xlsx')
df.head()

df = pandas.read_csv('tableau.csv')
df.head()

df = pandas.read_csv('tableau.csv', header=56)
df.head()

df = pandas.read_excel('meteoostende.xls')

url = ("http://opendata.digitalwallonia.be/dataset/"

df = pandas.read_excel(url)

len(df)

df.columns

df.head()

df.iloc[100]

df.mean()

s = df.icol(7)
s.head()

s.describe()

date = df.columns[1]
pression = df.columns[7]
df.plot(x=date, y=pression)

df.plot.hist(x=date, y=pression)

In [32]: s = Series(range(10))
In [31]: s
Out[31]:
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
dtype: int64

In [29]: s % 3 == 0
Out[29]:
0     True
1    False
2    False
3     True
4    False
5    False
6     True
7    False
8    False
9     True
dtype: bool

In [30]: s[s % 3 == 0]
Out[30]:
0    0
3    3
6    6
9    9
dtype: int64

est_noel = lambda date:date.day==25 and date.month==12

s_vrai_faux = df['Date'].apply(est_noel)
s_vrai_faux.tail(10)

df_noel = df[s_vrai_faux]
df_noel.icol([1,2])