In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')

In [2]:

datos_0 = pd.DataFrame([[0.5, 0.55, 0.7, 0.25, 0.56, 0.65, 0.39, 0.58, 0.62, 0.45],
                        [321, 475, 550, 299, 365, 700, 323, 457, 369, 457]])

print datos_0

       0       1      2       3       4       5       6       7       8  \
0    0.5    0.55    0.7    0.25    0.56    0.65    0.39    0.58    0.62   
1  321.0  475.00  550.0  299.00  365.00  700.00  323.00  457.00  369.00   

        9  
0    0.45  
1  457.00

In [3]:

datos_0 = datos_0.T

print datos_0

      0      1
0  0.50  321.0
1  0.55  475.0
2  0.70  550.0
3  0.25  299.0
4  0.56  365.0
5  0.65  700.0
6  0.39  323.0
7  0.58  457.0
8  0.62  369.0
9  0.45  457.0

In [4]:

datos_1 = pd.DataFrame({'coef_escorrentia': [0.5, 0.55, 0.7, 0.25, 0.56, 0.65, 0.39, 0.58, 0.62, 0.45],
                        'precipitacion': [321, 475, 550, 299, 365, 700, 323, 457, 369, 457]})

print datos_1

   coef_escorrentia  precipitacion
0              0.50            321
1              0.55            475
2              0.70            550
3              0.25            299
4              0.56            365
5              0.65            700
6              0.39            323
7              0.58            457
8              0.62            369
9              0.45            457

In [5]:

resultado = datos_1.describe()

print resultado

       coef_escorrentia  precipitacion
count         10.000000      10.000000
mean           0.525000     431.600000
std            0.133437     124.362552
min            0.250000     299.000000
25%            0.462500     333.500000
50%            0.555000     413.000000
75%            0.610000     470.500000
max            0.700000     700.000000

In [6]:

media_esc = resultado.loc['mean', 'coef_escorrentia']

print media_esc

0.525

In [7]:

p3_1 = datos_1.loc[2, 'precipitacion']

print p3_1

550.0

In [8]:

p3_2 = datos_1.iloc[2, 1]

print p3_2

550.0

In [9]:

mean_prec_4 = np.mean(datos_1.loc[0:4, 'precipitacion'])

print mean_prec_4

402.0

In [10]:

c_e = datos_1['coef_escorrentia']
prep = datos_1['precipitacion']

print c_e
print prep

0    0.50
1    0.55
2    0.70
3    0.25
4    0.56
5    0.65
6    0.39
7    0.58
8    0.62
9    0.45
Name: coef_escorrentia, dtype: float64
0    321
1    475
2    550
3    299
4    365
5    700
6    323
7    457
8    369
9    457
Name: precipitacion, dtype: int64

In [11]:

area = 600
volumen_anual = (c_e * prep / 1000 * area * 10e6) / 10e6  # hectometros cubicos

print volumen_anual

0     96.300
1    156.750
2    231.000
3     44.850
4    122.640
5    273.000
6     75.582
7    159.036
8    137.268
9    123.390
dtype: float64

In [12]:

volumen_total = np.sum(volumen_anual)

print volumen_total

1419.816

In [13]:

plt.figure('volumen_anual')
plt.plot(volumen_anual)
plt.show()

In [14]:

plt.figure('relacion_variables')
plt.scatter(c_e, prep, marker='o')
plt.xlabel('$c_e$')
plt.ylabel('precipitacion $(mm)$')
plt.show()

In [15]:

c = 0
for val in prep:
    if val > 500:
        c += 1  # c = c + 1

print c

In [16]:

clasificacion_anual = pd.Series(np.zeros(len(prep)))
for i, val in enumerate(volumen_anual):
    if val < 50:
        clasificacion_anual[i] = 1
    elif 50 <= val < 100:  # elif val >= 50 and val < 100
        clasificacion_anual[i] = 2
    elif 100 <= val < 150:
        clasificacion_anual[i] = 3
    elif val >= 150:
        clasificacion_anual[i] = 4

print clasificacion_anual

0    2.0
1    4.0
2    4.0
3    1.0
4    3.0
5    4.0
6    2.0
7    4.0
8    3.0
9    3.0
dtype: float64