Informe de aportes hidrologicos para el año 2004

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lll
kkk


    

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help(open)


    

    




Help on built-in function open in module io:

open(file, mode='r', buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None)
    Open file and return a stream.  Raise IOError upon failure.
    
    file is either a text or byte string giving the name (and the path
    if the file isn't in the current working directory) of the file to
    be opened or an integer file descriptor of the file to be
    wrapped. (If a file descriptor is given, it is closed when the
    returned I/O object is closed, unless closefd is set to False.)
    
    mode is an optional string that specifies the mode in which the file
    is opened. It defaults to 'r' which means open for reading in text
    mode.  Other common values are 'w' for writing (truncating the file if
    it already exists), 'x' for creating and writing to a new file, and
    'a' for appending (which on some Unix systems, means that all writes
    append to the end of the file regardless of the current seek position).
    In text mode, if encoding is not specified the encoding used is platform
    dependent: locale.getpreferredencoding(False) is called to get the
    current locale encoding. (For reading and writing raw bytes use binary
    mode and leave encoding unspecified.) The available modes are:
    
    ========= ===============================================================
    Character Meaning
    --------- ---------------------------------------------------------------
    'r'       open for reading (default)
    'w'       open for writing, truncating the file first
    'x'       create a new file and open it for writing
    'a'       open for writing, appending to the end of the file if it exists
    'b'       binary mode
    't'       text mode (default)
    '+'       open a disk file for updating (reading and writing)
    'U'       universal newline mode (deprecated)
    ========= ===============================================================
    
    The default mode is 'rt' (open for reading text). For binary random
    access, the mode 'w+b' opens and truncates the file to 0 bytes, while
    'r+b' opens the file without truncation. The 'x' mode implies 'w' and
    raises an `FileExistsError` if the file already exists.
    
    Python distinguishes between files opened in binary and text modes,
    even when the underlying operating system doesn't. Files opened in
    binary mode (appending 'b' to the mode argument) return contents as
    bytes objects without any decoding. In text mode (the default, or when
    't' is appended to the mode argument), the contents of the file are
    returned as strings, the bytes having been first decoded using a
    platform-dependent encoding or using the specified encoding if given.
    
    'U' mode is deprecated and will raise an exception in future versions
    of Python.  It has no effect in Python 3.  Use newline to control
    universal newlines mode.
    
    buffering is an optional integer used to set the buffering policy.
    Pass 0 to switch buffering off (only allowed in binary mode), 1 to select
    line buffering (only usable in text mode), and an integer > 1 to indicate
    the size of a fixed-size chunk buffer.  When no buffering argument is
    given, the default buffering policy works as follows:
    
    * Binary files are buffered in fixed-size chunks; the size of the buffer
      is chosen using a heuristic trying to determine the underlying device's
      "block size" and falling back on `io.DEFAULT_BUFFER_SIZE`.
      On many systems, the buffer will typically be 4096 or 8192 bytes long.
    
    * "Interactive" text files (files for which isatty() returns True)
      use line buffering.  Other text files use the policy described above
      for binary files.
    
    encoding is the name of the encoding used to decode or encode the
    file. This should only be used in text mode. The default encoding is
    platform dependent, but any encoding supported by Python can be
    passed.  See the codecs module for the list of supported encodings.
    
    errors is an optional string that specifies how encoding errors are to
    be handled---this argument should not be used in binary mode. Pass
    'strict' to raise a ValueError exception if there is an encoding error
    (the default of None has the same effect), or pass 'ignore' to ignore
    errors. (Note that ignoring encoding errors can lead to data loss.)
    See the documentation for codecs.register or run 'help(codecs.Codec)'
    for a list of the permitted encoding error strings.
    
    newline controls how universal newlines works (it only applies to text
    mode). It can be None, '', '\n', '\r', and '\r\n'.  It works as
    follows:
    
    * On input, if newline is None, universal newlines mode is
      enabled. Lines in the input can end in '\n', '\r', or '\r\n', and
      these are translated into '\n' before being returned to the
      caller. If it is '', universal newline mode is enabled, but line
      endings are returned to the caller untranslated. If it has any of
      the other legal values, input lines are only terminated by the given
      string, and the line ending is returned to the caller untranslated.
    
    * On output, if newline is None, any '\n' characters written are
      translated to the system default line separator, os.linesep. If
      newline is '' or '\n', no translation takes place. If newline is any
      of the other legal values, any '\n' characters written are translated
      to the given string.
    
    If closefd is False, the underlying file descriptor will be kept open
    when the file is closed. This does not work when a file name is given
    and must be True in that case.
    
    A custom opener can be used by passing a callable as *opener*. The
    underlying file descriptor for the file object is then obtained by
    calling *opener* with (*file*, *flags*). *opener* must return an open
    file descriptor (passing os.open as *opener* results in functionality
    similar to passing None).
    
    open() returns a file object whose type depends on the mode, and
    through which the standard file operations such as reading and writing
    are performed. When open() is used to open a file in a text mode ('w',
    'r', 'wt', 'rt', etc.), it returns a TextIOWrapper. When used to open
    a file in a binary mode, the returned class varies: in read binary
    mode, it returns a BufferedReader; in write binary and append binary
    modes, it returns a BufferedWriter, and in read/write mode, it returns
    a BufferedRandom.
    
    It is also possible to use a string or bytearray as a file for both
    reading and writing. For strings StringIO can be used like a file
    opened in a text mode, and for bytes a BytesIO can be used like a file
    opened in a binary mode.

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# %load demo.txt
ste es un demo que me permite la transformacion de datos
por medio de los conocimientos adquiridos en el diplomado


    

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# %load demo1.txt
hola
este es un documento


    

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%%writefile demo2.txt
linea 1
linea 2
linea 3
linea 4


    

    




Writing demo2.txt

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import pandas as pd
import statistics
import numpy as np


    

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# skiprows para saltar columnas al leer
x=pd.read_csv('AportesDiario_2004.csv', sep=';',decimal=',',thousands='.',skiprows=2)


    

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x.head()


    

    
Out[33]:






  

    

      

      
Fecha
      
Region Hidrologica
      
Nombre Rio
      
Aportes Caudal m3/s
      
Aportes Energia kWh
      
Aportes %
      
mes
    
  
  

    

      
0
      
1/01/2004
      
ANTIOQUIA
      
A. SAN LORENZO
      
17.56
      
3910000.0
      
72,27%
      
NaN
    
    

      
1
      
1/01/2004
      
ANTIOQUIA
      
CONCEPCION
      
6.15
      
1385300.0
      
123,69%
      
NaN
    
    

      
2
      
1/01/2004
      
ANTIOQUIA
      
DESV. EEPPM (NEC,PAJ,DOL)
      
11.43
      
2574700.0
      
147,13%
      
NaN
    
    

      
3
      
1/01/2004
      
ANTIOQUIA
      
GRANDE
      
18.65
      
4563500.0
      
79,23%
      
NaN
    
    

      
4
      
1/01/2004
      
ANTIOQUIA
      
GUADALUPE
      
11.28
      
2540900.0
      
80,15%
      
NaN
    
  

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x['Fecha']


    

    
Out[32]:





0        1/01/2004
1        1/01/2004
2        1/01/2004
3        1/01/2004
4        1/01/2004
5        1/01/2004
6        1/01/2004
7        1/01/2004
8        1/01/2004
9        1/01/2004
10       1/01/2004
11       1/01/2004
12       1/01/2004
13       1/01/2004
14       1/01/2004
15       1/01/2004
16       1/01/2004
17       1/01/2004
18       1/01/2004
19       1/01/2004
20       1/01/2004
21       1/01/2004
22       1/01/2004
23       1/01/2004
24       2/01/2004
25       2/01/2004
26       2/01/2004
27       2/01/2004
28       2/01/2004
29       2/01/2004
           ...    
8754    30/12/2004
8755    30/12/2004
8756    30/12/2004
8757    30/12/2004
8758    30/12/2004
8759    30/12/2004
8760    31/12/2004
8761    31/12/2004
8762    31/12/2004
8763    31/12/2004
8764    31/12/2004
8765    31/12/2004
8766    31/12/2004
8767    31/12/2004
8768    31/12/2004
8769    31/12/2004
8770    31/12/2004
8771    31/12/2004
8772    31/12/2004
8773    31/12/2004
8774    31/12/2004
8775    31/12/2004
8776    31/12/2004
8777    31/12/2004
8778    31/12/2004
8779    31/12/2004
8780    31/12/2004
8781    31/12/2004
8782    31/12/2004
8783    31/12/2004
Name: Fecha, dtype: object

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# x['Region Hidrologica'] == 'ANTIOQUIA' es una condicion y devuelve falsos y verdaderos y cuando hago 
#x[x['Region Hidrologica'] == 'ANTIOQUIA'] me devuelve los elementos que cumplen la condición
filtro=x[x['Region Hidrologica'] == 'ANTIOQUIA']


    

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len(filtro)


    

    
Out[42]:





4026

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# Set conjunto de datos que no estan repetidos
set(x['Nombre Rio'])


    

    
Out[43]:





{'A. SAN LORENZO',
 'ALTOANCHICAYA',
 'BATA',
 'BOGOTA N.R.',
 'CALIMA',
 'CAUCA SALVAJINA',
 'CHUZA',
 'CONCEPCION',
 'DESV. EEPPM (NEC,PAJ,DOL)',
 'DIGUA',
 'FLORIDA II',
 'GRANDE',
 'GUADALUPE',
 'GUATAPE',
 'GUAVIO',
 'MAGDALENA BETANIA',
 'MIEL I',
 'NARE',
 'OTROS RIOS (ESTIMADOS)',
 'PORCE II',
 'PRADO',
 'SAN CARLOS',
 'SINU URRA',
 'TENCHE'}

In [44]:

    

# A cada cambio en nombre rio (los agrupa con groupby) calculeme la media de las columnas numericas
x.groupby('Nombre Rio').mean()


    

    
Out[44]:






  

    

      

      
Aportes Caudal m3/s
      
Aportes Energia kWh
      
mes
    
    

      
Nombre Rio
      

      

      

    
  
  

    

      
A. SAN LORENZO
      
36.466612
      
8.105400e+06
      
NaN
    
    

      
ALTOANCHICAYA
      
43.468716
      
4.620596e+06
      
NaN
    
    

      
BATA
      
95.478989
      
1.571243e+07
      
NaN
    
    

      
BOGOTA N.R.
      
28.051803
      
1.277674e+07
      
NaN
    
    

      
CALIMA
      
11.922077
      
5.560117e+05
      
NaN
    
    

      
CAUCA SALVAJINA
      
113.523306
      
2.519614e+06
      
NaN
    
    

      
CHUZA
      
9.742486
      
4.337967e+06
      
NaN
    
    

      
CONCEPCION
      
6.606667
      
1.488170e+06
      
NaN
    
    

      
DESV. EEPPM (NEC,PAJ,DOL)
      
8.312240
      
2.291866e+06
      
NaN
    
    

      
DIGUA
      
27.529536
      
4.299954e+05
      
NaN
    
    

      
FLORIDA II
      
10.712486
      
2.148367e+05
      
NaN
    
    

      
GRANDE
      
27.511967
      
6.729973e+06
      
NaN
    
    

      
GUADALUPE
      
20.167978
      
4.542911e+06
      
NaN
    
    

      
GUATAPE
      
34.103033
      
5.624479e+06
      
NaN
    
    

      
GUAVIO
      
85.884563
      
2.062469e+07
      
NaN
    
    

      
MAGDALENA BETANIA
      
374.490765
      
5.580630e+06
      
NaN
    
    

      
MIEL I
      
78.433251
      
3.430428e+06
      
NaN
    
    

      
NARE
      
49.711612
      
1.738808e+07
      
NaN
    
    

      
OTROS RIOS (ESTIMADOS)
      
NaN
      
3.700457e+06
      
NaN
    
    

      
PORCE II
      
99.292650
      
4.976887e+06
      
NaN
    
    

      
PRADO
      
45.597268
      
4.984191e+05
      
NaN
    
    

      
SAN CARLOS
      
24.910492
      
3.084509e+06
      
NaN
    
    

      
SINU URRA
      
284.444208
      
3.165074e+06
      
NaN
    
    

      
TENCHE
      
3.900874
      
8.786683e+05
      
NaN
    
  

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n=set(x['Nombre Rio'])


    

In [97]:

    

rio=[]
media=[]
for y in n:
    z=x[x['Nombre Rio'] == y]['Aportes Energia kWh'] # para cada linea que cumpla la condicion de tener el mismo nobre de rio 
    # traigolos aportes de energia
    rio.append(y)
    
    if len(z) > 0:
        
        print(y, statistics.mean(z.values[:]))

        media2= np.array(media.append(statistics.mean(z.values[:])))


    

    




NARE 17388083.6066
TENCHE 878668.306011
GUAVIO 20624689.3443
MIEL I 3430428.4153
SINU URRA 3165074.04372
PRADO 498419.125683
FLORIDA II nan
SAN CARLOS 3084508.74317
ALTOANCHICAYA 4620595.90164
GUATAPE 5624479.23497
DESV. EEPPM (NEC,PAJ,DOL) nan
A. SAN LORENZO 8105399.72678
CHUZA nan
OTROS RIOS (ESTIMADOS) 3700456.8306
CALIMA 556011.748634
CAUCA SALVAJINA 2519614.48087
CONCEPCION 1488170.4918
GRANDE 6729973.22404
BATA 15712428.4153
BOGOTA N.R. nan
DIGUA 429995.355191
GUADALUPE 4542910.92896
PORCE II 4976887.15847
MAGDALENA BETANIA 5580630.32787

In [99]:

    

resultado=pd.DataFrame(data=media2,index=rio)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-99-55300f1bdaa4> in <module>()
----> 1 resultado=pd.DataFrame(data=media2,index=rio)

C:\Users\USUARIO\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
    253             else:
    254                 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 255                                          copy=copy)
    256         elif isinstance(data, (list, types.GeneratorType)):
    257             if isinstance(data, types.GeneratorType):

C:\Users\USUARIO\Anaconda3\lib\site-packages\pandas\core\frame.py in _init_ndarray(self, values, index, columns, dtype, copy)
    410         # by definition an array here
    411         # the dtypes will be coerced to a single dtype
--> 412         values = _prep_ndarray(values, copy=copy)
    413 
    414         if dtype is not None:

C:\Users\USUARIO\Anaconda3\lib\site-packages\pandas\core\frame.py in _prep_ndarray(values, copy)
   5323         values = values.reshape((values.shape[0], 1))
   5324     elif values.ndim != 2:
-> 5325         raise ValueError('Must pass 2-d input')
   5326 
   5327     return values

ValueError: Must pass 2-d input

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resultado


    

    
Out[93]:






  

    

      

      
0
    
  
  

    

      
NARE
      
1.738808e+07
    
    

      
TENCHE
      
8.786683e+05
    
    

      
GUAVIO
      
2.062469e+07
    
    

      
MIEL I
      
3.430428e+06
    
    

      
SINU URRA
      
3.165074e+06
    
    

      
PRADO
      
4.984191e+05
    
    

      
FLORIDA II
      
NaN
    
    

      
SAN CARLOS
      
3.084509e+06
    
    

      
ALTOANCHICAYA
      
4.620596e+06
    
    

      
GUATAPE
      
5.624479e+06
    
    

      
DESV. EEPPM (NEC,PAJ,DOL)
      
NaN
    
    

      
A. SAN LORENZO
      
8.105400e+06
    
    

      
CHUZA
      
NaN
    
    

      
OTROS RIOS (ESTIMADOS)
      
3.700457e+06
    
    

      
CALIMA
      
5.560117e+05
    
    

      
CAUCA SALVAJINA
      
2.519614e+06
    
    

      
CONCEPCION
      
1.488170e+06
    
    

      
GRANDE
      
6.729973e+06
    
    

      
BATA
      
1.571243e+07
    
    

      
BOGOTA N.R.
      
NaN
    
    

      
DIGUA
      
4.299954e+05
    
    

      
GUADALUPE
      
4.542911e+06
    
    

      
PORCE II
      
4.976887e+06
    
    

      
MAGDALENA BETANIA
      
5.580630e+06
    
  

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