In [274]:

    

import numpy as np
import pandas as pd
import math
import cmath
from scipy.optimize import root
import matplotlib.pyplot as plt
%matplotlib inline


    

In [275]:

    

print("Hola")


    

    




Hola

In [276]:

    

print("Hola"); print("Bonjour")


    

    




Hola
Bonjour

In [277]:

    

a = "Je m'appelle Agnès"
a


    

    
Out[277]:





"Je m'appelle Agnès"

In [278]:

    

a = "Je m'appelle\
Agnès"
a


    

    
Out[278]:





"Je m'appelleAgnès"

In [279]:

    

a = 0


    

In [280]:

    

for i in range(5):
    a+=1
    print(a)


    

    




1
2
3
4
5

In [281]:

    

a = np.array([1,2,3])
a


    

    
Out[281]:





array([1, 2, 3])

In [282]:

    

N,M=100,100
a = np.empty(10000).reshape(N,M)
b = np.random.rand(10000).reshape(N,M)
c = np.random.rand(10000).reshape(N,M)


    

In [283]:

    

%%timeit
a = b+c


    

    




20.6 µs ± 957 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [284]:

    

from __future__import braces


    

    




  File "<ipython-input-284-969d72d3c89b>", line 1
    from __future__import braces
                               ^
SyntaxError: invalid syntax

In [ ]:

    

a = np.array([[1,2,3],
    [4,5,6]
])
a


    

In [ ]:

    

a[0,0]


    

In [ ]:

    

a[0:2,1:3]


    

In [ ]:

    

np.zeros((3,4))


    

In [ ]:

    

np.identity(5).astype(int)


    

In [285]:

    

np.ones((3,4))


    

    
Out[285]:





array([[1., 1., 1., 1.],
       [1., 1., 1., 1.],
       [1., 1., 1., 1.]])

In [286]:

    

np.linspace(0,1,num=10)


    

    
Out[286]:





array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,
       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ])

In [287]:

    

np.logspace(0,3)


    

    
Out[287]:





array([   1.        ,    1.1513954 ,    1.32571137,    1.52641797,
          1.75751062,    2.02358965,    2.32995181,    2.6826958 ,
          3.0888436 ,    3.55648031,    4.09491506,    4.71486636,
          5.42867544,    6.25055193,    7.19685673,    8.28642773,
          9.54095476,   10.98541142,   12.64855217,   14.56348478,
         16.76832937,   19.30697729,   22.22996483,   25.59547923,
         29.47051703,   33.93221772,   39.06939937,   44.98432669,
         51.79474679,   59.63623317,   68.6648845 ,   79.06043211,
         91.0298178 ,  104.81131342,  120.67926406,  138.94954944,
        159.98587196,  184.20699693,  212.09508879,  244.20530945,
        281.1768698 ,  323.74575428,  372.75937203,  429.19342601,
        494.17133613,  568.9866029 ,  655.12855686,  754.31200634,
        868.51137375, 1000.        ])

In [288]:

    

x = np.linspace(0,1,num=5)
y = np.linspace(0,1,num=5)

xx,yy=np.meshgrid(x,y)


    

In [289]:

    

xx,yy


    

    
Out[289]:





(array([[0.  , 0.25, 0.5 , 0.75, 1.  ],
        [0.  , 0.25, 0.5 , 0.75, 1.  ],
        [0.  , 0.25, 0.5 , 0.75, 1.  ],
        [0.  , 0.25, 0.5 , 0.75, 1.  ],
        [0.  , 0.25, 0.5 , 0.75, 1.  ]]),
 array([[0.  , 0.  , 0.  , 0.  , 0.  ],
        [0.25, 0.25, 0.25, 0.25, 0.25],
        [0.5 , 0.5 , 0.5 , 0.5 , 0.5 ],
        [0.75, 0.75, 0.75, 0.75, 0.75],
        [1.  , 1.  , 1.  , 1.  , 1.  ]]))

In [290]:

    

a =np.arange(2*3).reshape(2,3)
a


    

    
Out[290]:





array([[0, 1, 2],
       [3, 4, 5]])

In [291]:

    

np.sqrt(a)


    

    
Out[291]:





array([[0.        , 1.        , 1.41421356],
       [1.73205081, 2.        , 2.23606798]])

In [292]:

    

np.sqrt(np.arange(-3,3))


    

    




C:\Users\Agnès\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in sqrt
  """Entry point for launching an IPython kernel.






    
Out[292]:





array([       nan,        nan,        nan, 0.        , 1.        ,
       1.41421356])

In [293]:

    

np.arange(-3,3).astype(complex)


    

    
Out[293]:





array([-3.+0.j, -2.+0.j, -1.+0.j,  0.+0.j,  1.+0.j,  2.+0.j])

In [294]:

    

np.sqrt(_)


    

    
Out[294]:





array([0.        +1.73205081j, 0.        +1.41421356j,
       0.        +1.        j, 0.        +0.        j,
       1.        +0.        j, 1.41421356+0.        j])

In [295]:

    

a = np.arange(6)
b = np.ones(6).astype(int)
a,b


    

    
Out[295]:





(array([0, 1, 2, 3, 4, 5]), array([1, 1, 1, 1, 1, 1]))

In [296]:

    

a<b


    

    
Out[296]:





array([ True, False, False, False, False, False])

In [297]:

    

np.any(a<b)


    

    
Out[297]:





True

In [298]:

    

np.all(a<b)


    

    
Out[298]:





False

In [299]:

    

a =np.arange(6).astype(float)
b =np.ones(6)
a,b


    

    
Out[299]:





(array([0., 1., 2., 3., 4., 5.]), array([1., 1., 1., 1., 1., 1.]))

In [300]:

    

np.isclose(a,b,rtol=1e-6)


    

    
Out[300]:





array([False,  True, False, False, False, False])

In [301]:

    

np.allclose(a,b,rtol=1e-6)


    

    
Out[301]:





False

In [302]:

    

a = np.zeros((3,4))
a


    

    
Out[302]:





array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])

In [303]:

    

a[0,:]=1
a


    

    
Out[303]:





array([[1., 1., 1., 1.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])

In [304]:

    

b=np.zeros((3,4))
b[-1]=np.arange(5,9)
b


    

    
Out[304]:





array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [5., 6., 7., 8.]])

In [305]:

    

c = np.ones(10)
c


    

    
Out[305]:





array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

In [306]:

    

c[::2]=2
c


    

    
Out[306]:





array([2., 1., 2., 1., 2., 1., 2., 1., 2., 1.])

In [307]:

    

tablera =np.zeros((8,8))
tablera


    

    
Out[307]:





array([[0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.]])

In [308]:

    

tablera[:,::2]=1
tablera[::2,1::2]=1
tablera


    

    
Out[308]:





array([[1., 1., 1., 1., 1., 1., 1., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.],
       [1., 1., 1., 1., 1., 1., 1., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.],
       [1., 1., 1., 1., 1., 1., 1., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.],
       [1., 1., 1., 1., 1., 1., 1., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.]])

In [309]:

    

plt.plot([0,0.1,0.2,0.5,0.6],[1,-1,0,3,-1])


    

    
Out[309]:





[<matplotlib.lines.Line2D at 0x1609c35cbe0>]






    

In [310]:

    

def f(x):
    return np.exp(-x**2)


    

In [311]:

    

x=np.linspace(-1,5,num=1000)


    

In [312]:

    

plt.plot(x,f(x),label="f(x)")
plt.xlabel("x")
plt.ylabel("y")
plt.legend("Fonction x")


    

    
Out[312]:





<matplotlib.legend.Legend at 0x1609c3313c8>






    

In [313]:

    

x


    

    
Out[313]:





array([-1.00000000e+00, -9.93993994e-01, -9.87987988e-01, -9.81981982e-01,
       -9.75975976e-01, -9.69969970e-01, -9.63963964e-01, -9.57957958e-01,
       -9.51951952e-01, -9.45945946e-01, -9.39939940e-01, -9.33933934e-01,
       -9.27927928e-01, -9.21921922e-01, -9.15915916e-01, -9.09909910e-01,
       -9.03903904e-01, -8.97897898e-01, -8.91891892e-01, -8.85885886e-01,
       -8.79879880e-01, -8.73873874e-01, -8.67867868e-01, -8.61861862e-01,
       -8.55855856e-01, -8.49849850e-01, -8.43843844e-01, -8.37837838e-01,
       -8.31831832e-01, -8.25825826e-01, -8.19819820e-01, -8.13813814e-01,
       -8.07807808e-01, -8.01801802e-01, -7.95795796e-01, -7.89789790e-01,
       -7.83783784e-01, -7.77777778e-01, -7.71771772e-01, -7.65765766e-01,
       -7.59759760e-01, -7.53753754e-01, -7.47747748e-01, -7.41741742e-01,
       -7.35735736e-01, -7.29729730e-01, -7.23723724e-01, -7.17717718e-01,
       -7.11711712e-01, -7.05705706e-01, -6.99699700e-01, -6.93693694e-01,
       -6.87687688e-01, -6.81681682e-01, -6.75675676e-01, -6.69669670e-01,
       -6.63663664e-01, -6.57657658e-01, -6.51651652e-01, -6.45645646e-01,
       -6.39639640e-01, -6.33633634e-01, -6.27627628e-01, -6.21621622e-01,
       -6.15615616e-01, -6.09609610e-01, -6.03603604e-01, -5.97597598e-01,
       -5.91591592e-01, -5.85585586e-01, -5.79579580e-01, -5.73573574e-01,
       -5.67567568e-01, -5.61561562e-01, -5.55555556e-01, -5.49549550e-01,
       -5.43543544e-01, -5.37537538e-01, -5.31531532e-01, -5.25525526e-01,
       -5.19519520e-01, -5.13513514e-01, -5.07507508e-01, -5.01501502e-01,
       -4.95495495e-01, -4.89489489e-01, -4.83483483e-01, -4.77477477e-01,
       -4.71471471e-01, -4.65465465e-01, -4.59459459e-01, -4.53453453e-01,
       -4.47447447e-01, -4.41441441e-01, -4.35435435e-01, -4.29429429e-01,
       -4.23423423e-01, -4.17417417e-01, -4.11411411e-01, -4.05405405e-01,
       -3.99399399e-01, -3.93393393e-01, -3.87387387e-01, -3.81381381e-01,
       -3.75375375e-01, -3.69369369e-01, -3.63363363e-01, -3.57357357e-01,
       -3.51351351e-01, -3.45345345e-01, -3.39339339e-01, -3.33333333e-01,
       -3.27327327e-01, -3.21321321e-01, -3.15315315e-01, -3.09309309e-01,
       -3.03303303e-01, -2.97297297e-01, -2.91291291e-01, -2.85285285e-01,
       -2.79279279e-01, -2.73273273e-01, -2.67267267e-01, -2.61261261e-01,
       -2.55255255e-01, -2.49249249e-01, -2.43243243e-01, -2.37237237e-01,
       -2.31231231e-01, -2.25225225e-01, -2.19219219e-01, -2.13213213e-01,
       -2.07207207e-01, -2.01201201e-01, -1.95195195e-01, -1.89189189e-01,
       -1.83183183e-01, -1.77177177e-01, -1.71171171e-01, -1.65165165e-01,
       -1.59159159e-01, -1.53153153e-01, -1.47147147e-01, -1.41141141e-01,
       -1.35135135e-01, -1.29129129e-01, -1.23123123e-01, -1.17117117e-01,
       -1.11111111e-01, -1.05105105e-01, -9.90990991e-02, -9.30930931e-02,
       -8.70870871e-02, -8.10810811e-02, -7.50750751e-02, -6.90690691e-02,
       -6.30630631e-02, -5.70570571e-02, -5.10510511e-02, -4.50450450e-02,
       -3.90390390e-02, -3.30330330e-02, -2.70270270e-02, -2.10210210e-02,
       -1.50150150e-02, -9.00900901e-03, -3.00300300e-03,  3.00300300e-03,
        9.00900901e-03,  1.50150150e-02,  2.10210210e-02,  2.70270270e-02,
        3.30330330e-02,  3.90390390e-02,  4.50450450e-02,  5.10510511e-02,
        5.70570571e-02,  6.30630631e-02,  6.90690691e-02,  7.50750751e-02,
        8.10810811e-02,  8.70870871e-02,  9.30930931e-02,  9.90990991e-02,
        1.05105105e-01,  1.11111111e-01,  1.17117117e-01,  1.23123123e-01,
        1.29129129e-01,  1.35135135e-01,  1.41141141e-01,  1.47147147e-01,
        1.53153153e-01,  1.59159159e-01,  1.65165165e-01,  1.71171171e-01,
        1.77177177e-01,  1.83183183e-01,  1.89189189e-01,  1.95195195e-01,
        2.01201201e-01,  2.07207207e-01,  2.13213213e-01,  2.19219219e-01,
        2.25225225e-01,  2.31231231e-01,  2.37237237e-01,  2.43243243e-01,
        2.49249249e-01,  2.55255255e-01,  2.61261261e-01,  2.67267267e-01,
        2.73273273e-01,  2.79279279e-01,  2.85285285e-01,  2.91291291e-01,
        2.97297297e-01,  3.03303303e-01,  3.09309309e-01,  3.15315315e-01,
        3.21321321e-01,  3.27327327e-01,  3.33333333e-01,  3.39339339e-01,
        3.45345345e-01,  3.51351351e-01,  3.57357357e-01,  3.63363363e-01,
        3.69369369e-01,  3.75375375e-01,  3.81381381e-01,  3.87387387e-01,
        3.93393393e-01,  3.99399399e-01,  4.05405405e-01,  4.11411411e-01,
        4.17417417e-01,  4.23423423e-01,  4.29429429e-01,  4.35435435e-01,
        4.41441441e-01,  4.47447447e-01,  4.53453453e-01,  4.59459459e-01,
        4.65465465e-01,  4.71471471e-01,  4.77477477e-01,  4.83483483e-01,
        4.89489489e-01,  4.95495495e-01,  5.01501502e-01,  5.07507508e-01,
        5.13513514e-01,  5.19519520e-01,  5.25525526e-01,  5.31531532e-01,
        5.37537538e-01,  5.43543544e-01,  5.49549550e-01,  5.55555556e-01,
        5.61561562e-01,  5.67567568e-01,  5.73573574e-01,  5.79579580e-01,
        5.85585586e-01,  5.91591592e-01,  5.97597598e-01,  6.03603604e-01,
        6.09609610e-01,  6.15615616e-01,  6.21621622e-01,  6.27627628e-01,
        6.33633634e-01,  6.39639640e-01,  6.45645646e-01,  6.51651652e-01,
        6.57657658e-01,  6.63663664e-01,  6.69669670e-01,  6.75675676e-01,
        6.81681682e-01,  6.87687688e-01,  6.93693694e-01,  6.99699700e-01,
        7.05705706e-01,  7.11711712e-01,  7.17717718e-01,  7.23723724e-01,
        7.29729730e-01,  7.35735736e-01,  7.41741742e-01,  7.47747748e-01,
        7.53753754e-01,  7.59759760e-01,  7.65765766e-01,  7.71771772e-01,
        7.77777778e-01,  7.83783784e-01,  7.89789790e-01,  7.95795796e-01,
        8.01801802e-01,  8.07807808e-01,  8.13813814e-01,  8.19819820e-01,
        8.25825826e-01,  8.31831832e-01,  8.37837838e-01,  8.43843844e-01,
        8.49849850e-01,  8.55855856e-01,  8.61861862e-01,  8.67867868e-01,
        8.73873874e-01,  8.79879880e-01,  8.85885886e-01,  8.91891892e-01,
        8.97897898e-01,  9.03903904e-01,  9.09909910e-01,  9.15915916e-01,
        9.21921922e-01,  9.27927928e-01,  9.33933934e-01,  9.39939940e-01,
        9.45945946e-01,  9.51951952e-01,  9.57957958e-01,  9.63963964e-01,
        9.69969970e-01,  9.75975976e-01,  9.81981982e-01,  9.87987988e-01,
        9.93993994e-01,  1.00000000e+00,  1.00600601e+00,  1.01201201e+00,
        1.01801802e+00,  1.02402402e+00,  1.03003003e+00,  1.03603604e+00,
        1.04204204e+00,  1.04804805e+00,  1.05405405e+00,  1.06006006e+00,
        1.06606607e+00,  1.07207207e+00,  1.07807808e+00,  1.08408408e+00,
        1.09009009e+00,  1.09609610e+00,  1.10210210e+00,  1.10810811e+00,
        1.11411411e+00,  1.12012012e+00,  1.12612613e+00,  1.13213213e+00,
        1.13813814e+00,  1.14414414e+00,  1.15015015e+00,  1.15615616e+00,
        1.16216216e+00,  1.16816817e+00,  1.17417417e+00,  1.18018018e+00,
        1.18618619e+00,  1.19219219e+00,  1.19819820e+00,  1.20420420e+00,
        1.21021021e+00,  1.21621622e+00,  1.22222222e+00,  1.22822823e+00,
        1.23423423e+00,  1.24024024e+00,  1.24624625e+00,  1.25225225e+00,
        1.25825826e+00,  1.26426426e+00,  1.27027027e+00,  1.27627628e+00,
        1.28228228e+00,  1.28828829e+00,  1.29429429e+00,  1.30030030e+00,
        1.30630631e+00,  1.31231231e+00,  1.31831832e+00,  1.32432432e+00,
        1.33033033e+00,  1.33633634e+00,  1.34234234e+00,  1.34834835e+00,
        1.35435435e+00,  1.36036036e+00,  1.36636637e+00,  1.37237237e+00,
        1.37837838e+00,  1.38438438e+00,  1.39039039e+00,  1.39639640e+00,
        1.40240240e+00,  1.40840841e+00,  1.41441441e+00,  1.42042042e+00,
        1.42642643e+00,  1.43243243e+00,  1.43843844e+00,  1.44444444e+00,
        1.45045045e+00,  1.45645646e+00,  1.46246246e+00,  1.46846847e+00,
        1.47447447e+00,  1.48048048e+00,  1.48648649e+00,  1.49249249e+00,
        1.49849850e+00,  1.50450450e+00,  1.51051051e+00,  1.51651652e+00,
        1.52252252e+00,  1.52852853e+00,  1.53453453e+00,  1.54054054e+00,
        1.54654655e+00,  1.55255255e+00,  1.55855856e+00,  1.56456456e+00,
        1.57057057e+00,  1.57657658e+00,  1.58258258e+00,  1.58858859e+00,
        1.59459459e+00,  1.60060060e+00,  1.60660661e+00,  1.61261261e+00,
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        4.88588589e+00,  4.89189189e+00,  4.89789790e+00,  4.90390390e+00,
        4.90990991e+00,  4.91591592e+00,  4.92192192e+00,  4.92792793e+00,
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        4.95795796e+00,  4.96396396e+00,  4.96996997e+00,  4.97597598e+00,
        4.98198198e+00,  4.98798799e+00,  4.99399399e+00,  5.00000000e+00])

In [314]:

    

f(x)


    

    
Out[314]:





array([3.67879441e-01, 3.72311630e-01, 3.76770035e-01, 3.81254323e-01,
       3.85764150e-01, 3.90299166e-01, 3.94859006e-01, 3.99443301e-01,
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       4.22717894e-01, 4.27440565e-01, 4.32184817e-01, 4.36950202e-01,
       4.41736262e-01, 4.46542528e-01, 4.51368524e-01, 4.56213762e-01,
       4.61077746e-01, 4.65959971e-01, 4.70859921e-01, 4.75777073e-01,
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       9.98909414e-01, 9.98477114e-01, 9.97973001e-01, 9.97397183e-01,
       9.96749786e-01, 9.96030948e-01, 9.95240825e-01, 9.94379587e-01,
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       5.42346689e-11, 5.11561918e-11, 4.82489747e-11, 4.55036924e-11,
       4.29115159e-11, 4.04640868e-11, 3.81534924e-11, 3.59722431e-11,
       3.39132500e-11, 3.19698037e-11, 3.01355552e-11, 2.84044963e-11,
       2.67709422e-11, 2.52295142e-11, 2.37751238e-11, 2.24029575e-11,
       2.11084620e-11, 1.98873307e-11, 1.87354905e-11, 1.76490896e-11,
       1.66244856e-11, 1.56582345e-11, 1.47470801e-11, 1.38879439e-11])

In [315]:

    

fig,axes=plt.subplots()

axes.plot(x,f(x),'ro',label="Fonction x")
axes.set_xlim(-2,4)
axes.set_ylim(-1,2)

fig.savefig("Graphique1.png")


    

In [316]:

    

fig,axes=plt.subplots(1,2)

axes[0].plot(x,f(x))
axes[1].plot(x,-f(x),'r')


    

    
Out[316]:





[<matplotlib.lines.Line2D at 0x1609c492e48>]






    

In [317]:

    

axes


    

    
Out[317]:





array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001609A737390>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000001609C460EF0>],
      dtype=object)

In [318]:

    

x = np.random.randn(100)
y = np.random.randn(100)

s = 20*np.random.randn(100)
c=np.random.randn(100)

plt.scatter(x,y,s,c)


    

    




C:\Users\Agnès\Anaconda3\lib\site-packages\matplotlib\collections.py:853: RuntimeWarning: invalid value encountered in sqrt
  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor






    
Out[318]:





<matplotlib.collections.PathCollection at 0x1609c4ff470>






    

In [319]:

    

x =np.linspace(-2,2)
y = np.linspace(-2,2)

xx,yy=np.meshgrid(x,y)


    

In [ ]:

    

xx,yy


    

In [ ]:

    

def g(x,y):
    return np.cos(x)+np.sin(y)**2


    

In [ ]:

    

zz=g(xx,yy)


    

In [ ]:

    

gof,axes=plt.subplots()
axes.contour(xx,yy,zz)


    

In [ ]:

    

def f(x,A,B):
    return np.exp(-B*x**2)


    

In [ ]:

    

integrate.quad(f,0,5,args=(1.0,1.0))


    

In [ ]:

    

from scipy import integrate


    

In [ ]:

    

def f(x,t):
    return -x


    

In [ ]:

    

x0=1.0


    

In [ ]:

    

t =np.linspace(0,3)


    

In [ ]:

    

sol = integrate.odeint(f,x0,t)


    

In [ ]:

    

plt.plot(t,sol)


    

In [ ]:

    

def f(x,t):
    return np.array([x[1],-x[0]])


    

In [ ]:

    

t = np.linspace(0,10)
t


    

In [ ]:

    

x0=np.array([1.0,0.0])
x0


    

In [ ]:

    

sol =integrate.odeint(f,x0,t)


    

In [ ]:

    

plt.plot(t, sol[:, 0])
plt.plot(t,sol[:,1],"--")


    

In [ ]:

    

from scipy import optimize


    

In [ ]:

    

def F(E,e, M):
    return E- e*np.sin(E)-M


    

In [ ]:

    

optimize.root(F,0.1, args=(0.016,0.1))


    

In [ ]:

    

from scipy import interpolate


    

In [ ]:

    

from scipy.interpolate import barycentric_interpolate
from numpy.polynomial import chebyshev
from scipy.interpolate import InterpolatedUnivariateSpline


    

In [ ]:

    

def fonction(x,A,B,C):
    return A*np.exp(-B*x**2)+C


    

In [ ]:

    

datos = {"Nombre":["Agnès","Julie","Daisy","Christien"],"Qualifications": ["100","90","100","80"],"Deportes " :["Football","Natation","Rugby","Basketball"],"Matières ": ["Méthodes","Cuisiner","Chimie","Manger"] }


    

In [ ]:

    

df=pd.DataFrame(datos)


    

In [ ]:

    

print(df)


    

In [ ]:

    

print('\n'*2)


    

In [ ]:

    

datos2 = {"Nombre":["Agnès","Julie","Daisy","Christien"],"Qualifications": ["100","90",np.nan,"80"],"Deportes " :["Football","N/A","Rugby","Basketball"],"Matières ": ["Méthodes","N/A","Chimie","Manger"] }


    

In [ ]:

    

df2=pd.DataFrame(datos2)


    

In [ ]:

    

print(df2)
print('\n'*2)
print(df2.info())


    

In [ ]:

    

nuevo = pd.DataFrame(df2)
nuevo = nuevo.dropna(how='any',inplace=True)
print(nuevo)


    

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