
print('Hello World!')

Hello World!


print("Hello World!")

Hello World!


ss = "Haa'ha"
print(ss)

Haa'ha


name = input("Please enter your name: ")


print(name)

Michael


! python --version

Python 3.9.4


# ! 命令可以在notebook中执行命令窗口的命令
! ls

Python_intro.html  foooo.py           lec-1.key          my_package
Python_intro.ipynb hello.py           lec-1.pdf


# This is a comment
a = 10
if a > 0:
    print(a + 3)
else:
    print(-a)

13


# This is ok, but not recommended.
a = 10
if a > 0:
  print(a + 3)
else:
  print(-a)

13

10000000

10000000


10_000_000

10000000


0xa5b3

42419


0.23

0.23

1e5

100000.0


"asdias"

'asdias'


'I have a \'naive\' dream.'

"I have a 'naive' dream."


print("I have a 'naive' dream.")

I have a 'naive' dream.


print('This is a cat. \n That is a dog.')

This is a cat. 
 That is a dog.


print('This is a cat. \t That is a dog.')

This is a cat. 	 That is a dog.


print("This is a cat. \ That is a dog.")

This is a cat. \ That is a dog.


print("This is a cat. \\ That is a dog.")

This is a cat. \ That is a dog.


print("This is a cat. \\\\\\ That is a dog.")

This is a cat. \\\ That is a dog.


print(r'This is a cat. \t That is a dog.')

This is a cat. \t That is a dog.


print("line1 \
       line2 \
       line3")

line1        line2        line3


print("""line1
line2
line3
""")

line1
line2
line3


name

'Michael'


name[0:3]

'Mic'


for letter in name:
    print(letter)

M
i
c
h
a
e
l


name = 'Martin Luther King'


'%s has a dream.' % name

'Martin Luther King has a dream.'


n = 1e6
'%s has %d supporters.' % (name, n)

'Martin Luther King has 1000000 supporters.'


'%s has %.2f yuan' % ('Michael', 1234.324256)

'Michael has 1234.32 yuan'


# format()
'Bingo, that {0} has {1} seats.'.format('classroom', 35)

'Bingo, that classroom has 35 seats.'


name = 'Lucy'


# f-string
f"{name} has a dream"

'Lucy has a dream'


print("中文")

中文


# ord()获取字符的整数，chr()把编码转换为对应的字符
ord('中')

20013


ord('A')

65


chr(12475)

'セ'


'ABC'

'ABC'


b'ABC'

b'ABC'


'中文'.encode('utf-8')

b'\xe4\xb8\xad\xe6\x96\x87'


'中文'.encode('ascii')

---------------------------------------------------------------------------
UnicodeEncodeError                        Traceback (most recent call last)
<ipython-input-44-76f41cd8dafa> in <module>
----> 1 '中文'.encode('ascii')

UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-1: ordinal not in range(128)


'中文'.encode('GB2312')

b'\xd6\xd0\xce\xc4'


# 若编码不对应，则会出错
'中文'.encode('GB2312').decode('utf-8')

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-46-64c137ca29fd> in <module>
      1 # 若编码不对应，则会出错
----> 2 '中文'.encode('GB2312').decode('utf-8')

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd6 in position 0: invalid continuation byte


b'\xe4\xb8\xad\xe6\x96\x87'.decode('utf-8')

'中文'


#!/usr/bin/env python3
# -*- coding: utf-8 -*-


import pandas as pd
pd.read_csv('../../../data/AF_Co.txt',sep='\t')


pd.read_csv('../../../data/AF_Co.txt',sep='\t',encoding='GB2312')

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-50-fd9b9c2e2b76> in <module>
----> 1 pd.read_csv('../../../data/AF_Co.txt',sep='\t',encoding='GB2312')

~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers.py in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    608     kwds.update(kwds_defaults)
    609 
--> 610     return _read(filepath_or_buffer, kwds)
    611 
    612 

~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    460 
    461     # Create the parser.
--> 462     parser = TextFileReader(filepath_or_buffer, **kwds)
    463 
    464     if chunksize or iterator:

~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
    817             self.options["has_index_names"] = kwds["has_index_names"]
    818 
--> 819         self._engine = self._make_engine(self.engine)
    820 
    821     def close(self):

~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
   1048             )
   1049         # error: Too many arguments for "ParserBase"
-> 1050         return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
   1051 
   1052     def _failover_to_python(self):

~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
   1896 
   1897         try:
-> 1898             self._reader = parsers.TextReader(self.handles.handle, **kwds)
   1899         except Exception:
   1900             self.handles.close()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._get_header()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()

pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()

UnicodeDecodeError: 'gb2312' codec can't decode byte 0x95 in position 221129: illegal multibyte sequence


pd.read_csv('../../../data/AF_Co.txt',sep='\t',encoding='GBK')


True

True


False

False


True and True

True


True or False

True


False and True

False


5 > 2 and 1 > 3

False


not True

False


(5 > 2) & (3 > 2)

True


5 > 2 & 3 > 2

False


5 > 2 and 3 > 2

True


5 > 2 or 2 > 3

True


5 > 2 | 3 > 2 # why?

True


None


a = 1


b = 'abs'


c = True


a = 'asd'


a = 12
a = a + 13


a += 13


print(a)

38


a = 'asd'


a = 'asd'
b = a
a = 'QWE'


print(a)

QWE


print(b)

asd


START = 2000


PI = 3.14


3 / 2

1.5


3 // 2

1


10 % 3

1


students = ['Albert',[1,2,3],'Calvin']


students

['Albert', [1, 2, 3], 'Calvin']


students[0]

'Albert'


# slicing
students[0:3]

['Albert', [1, 2, 3], 'Calvin']


import pandas as pd


df = pd.read_pickle('../../../data/stk_df_2015_2021.pkl')

df


df.loc[0:3]


df.iloc[0:3]


del df


students[-1]

'Calvin'


students.append('Steve')


students

['Albert', [1, 2, 3], 'Calvin', 'Steve']


students.insert(1,'Hey')


students

['Albert', 'Hey', [1, 2, 3], 'Calvin', 'Steve']


students.pop()
students

['Albert', 'Hey', [1, 2, 3], 'Calvin']


students.pop(1)
students

['Albert', [1, 2, 3], 'Calvin']


students

['Albert', [1, 2, 3], 'Calvin']


students[2] = 'HeyHey'
students

['Albert', [1, 2, 3], 'HeyHey']


list_ = ['a','b',[12,13]]
list_

['a', 'b', [12, 13]]


list_[2][1]

13


t = (1,2,3)


t[0]

1


t[1]

2


t[0:3]

(1, 2, 3)


t[0] = 123

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-106-b3a814649781> in <module>
----> 1 t[0] = 123

TypeError: 'tuple' object does not support item assignment


t = (1)

t

1


t = (1,)

t

(1,)


t = (1,2,'a')

t

(1, 2, 'a')


list_ = ['a', 'b']
t = (1,2,list_)
print(t)

(1, 2, ['a', 'b'])


t[2][0] = 'x'
t[2][1] = 'y'
t

(1, 2, ['x', 'y'])


list_[0] = 'xx'
list_[1] = 'yy'
t

(1, 2, ['xx', 'yy'])


list2 = list_
list2

['xx', 'yy']


list_[0] = 'xxx'
list_[1] = 'yyy'
list2

['xxx', 'yyy']


s = 'Hello'
list_ = ['a', 'b']
t = (s,2,list_)
print(t)

('Hello', 2, ['a', 'b'])


s = 'Hey'
print(t)

('Hello', 2, ['a', 'b'])


ss = s
t = (ss,2,list_)
print(t)

('Hey', 2, ['a', 'b'])


s = 'HeyHey'
print(t)

('Hey', 2, ['a', 'b'])


age = 20
if age >= 18:
    print('your age is', age)
    print('adult')

your age is 20
adult


age = 3
if age >= 18:
    print('adult')
elif age >= 6:
    print('teenager')
else:
    print('kid')

kid


age = 20
if age >= 6:
    print('teenager')
elif age >= 18:
    print('adult')
else:
    print('kid')

teenager


if 378:
    print('haha')

haha


len([])

0


if []:
    print('haha')
else:
    print('hehe')

hehe


for i in 'abc':
    print(i)

a
b
c


list_ = ['Michael','Miller']
for i in list_:
    print(i)

Michael
Miller


n = 1
sum = 0
while n <= 100:
    sum += n # same as sum = sum + n
    n = n + 1
print(sum)

5050


# break
n = 1
sum = 0
while n <= 100:
    print(n)
    sum += n # same as sum = sum + n
    n = n + 1
    if n == 10:
        break


print(sum)

45


# continue
n = 0
while n <= 10:
    n = n + 1
    if n % 2 == 0:
        continue
    print(n)


# n = 0
# while n < 10:
#     if n % 2 == 0:
#         continue
#     n = n + 1


d = {'a':1,'b':2}


d['a']

1


d[0]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-137-123a9cc6df61> in <module>
----> 1 d[0]

KeyError: 0


d['c'] = 'asd'

d

{'a': 1, 'b': 2, 'c': 'asd'}


d[0] = 'haha'

d

{'a': 1, 'b': 2, 'c': 'asd', 0: 'haha'}


d[0]

'haha'


'name' in d

False


d.keys()

dict_keys(['a', 'b', 'c', 0])


d.items()

dict_items([('a', 1), ('b', 2), ('c', 'asd'), (0, 'haha')])


d.items()[0]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-146-62707639c934> in <module>
----> 1 d.items()[0]

TypeError: 'dict_items' object is not subscriptable


list(d.items())[0]

('a', 1)


d.values()

dict_values([1, 2, 'asd', 'haha'])


d.values()[0]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-149-d8a1069851b8> in <module>
----> 1 d.values()[0]

TypeError: 'dict_values' object is not subscriptable


list(d.values())[0]

1


{['a']:3}

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-151-ae1f4c077df5> in <module>
----> 1 {['a']:3}

TypeError: unhashable type: 'list'


s = set([1,1,1,2,3,4,4])
s

{1, 2, 3, 4}


s.add(5)

s

{1, 2, 3, 4, 5}


s.remove(3)
s

{1, 2, 4, 5}


ss = set([4,5,6])


s = set([5,6,7])


s.intersection(ss)

{5, 6}


list_ = [3,1,2]


list_.sort()


list_

[1, 2, 3]


a = 'xyz'


a.replace('x','X')

'Xyz'

a

'xyz'


b = a.replace('x','X')
b

'Xyz'


abs(-1)

1


?abs

Signature: abs(x, /)
Docstring: Return the absolute value of the argument.
Type:      builtin_function_or_method


?pd.read_csv

Signature:
pd.read_csv(
    filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap],
    sep=<object object at 0x7fbb1173e110>,
    delimiter=None,
    header='infer',
    names=None,
    index_col=None,
    usecols=None,
    squeeze=False,
    prefix=None,
    mangle_dupe_cols=True,
    dtype=None,
    engine=None,
    converters=None,
    true_values=None,
    false_values=None,
    skipinitialspace=False,
    skiprows=None,
    skipfooter=0,
    nrows=None,
    na_values=None,
    keep_default_na=True,
    na_filter=True,
    verbose=False,
    skip_blank_lines=True,
    parse_dates=False,
    infer_datetime_format=False,
    keep_date_col=False,
    date_parser=None,
    dayfirst=False,
    cache_dates=True,
    iterator=False,
    chunksize=None,
    compression='infer',
    thousands=None,
    decimal: str = '.',
    lineterminator=None,
    quotechar='"',
    quoting=0,
    doublequote=True,
    escapechar=None,
    comment=None,
    encoding=None,
    dialect=None,
    error_bad_lines=True,
    warn_bad_lines=True,
    delim_whitespace=False,
    low_memory=True,
    memory_map=False,
    float_precision=None,
    storage_options: Optional[Dict[str, Any]] = None,
)
Docstring:
Read a comma-separated values (csv) file into DataFrame.

Also supports optionally iterating or breaking of the file
into chunks.

Additional help can be found in the online docs for
`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.

Parameters
----------
filepath_or_buffer : str, path object or file-like object
    Any valid string path is acceptable. The string could be a URL. Valid
    URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
    expected. A local file could be: file://localhost/path/to/table.csv.

    If you want to pass in a path object, pandas accepts any ``os.PathLike``.

    By file-like object, we refer to objects with a ``read()`` method, such as
    a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default ','
    Delimiter to use. If sep is None, the C engine cannot automatically detect
    the separator, but the Python parsing engine can, meaning the latter will
    be used and automatically detect the separator by Python's builtin sniffer
    tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
    different from ``'\s+'`` will be interpreted as regular expressions and
    will also force the use of the Python parsing engine. Note that regex
    delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, default ``None``
    Alias for sep.
header : int, list of int, default 'infer'
    Row number(s) to use as the column names, and the start of the
    data.  Default behavior is to infer the column names: if no names
    are passed the behavior is identical to ``header=0`` and column
    names are inferred from the first line of the file, if column
    names are passed explicitly then the behavior is identical to
    ``header=None``. Explicitly pass ``header=0`` to be able to
    replace existing names. The header can be a list of integers that
    specify row locations for a multi-index on the columns
    e.g. [0,1,3]. Intervening rows that are not specified will be
    skipped (e.g. 2 in this example is skipped). Note that this
    parameter ignores commented lines and empty lines if
    ``skip_blank_lines=True``, so ``header=0`` denotes the first line of
    data rather than the first line of the file.
names : array-like, optional
    List of column names to use. If the file contains a header row,
    then you should explicitly pass ``header=0`` to override the column names.
    Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, default ``None``
  Column(s) to use as the row labels of the ``DataFrame``, either given as
  string name or column index. If a sequence of int / str is given, a
  MultiIndex is used.

  Note: ``index_col=False`` can be used to force pandas to *not* use the first
  column as the index, e.g. when you have a malformed file with delimiters at
  the end of each line.
usecols : list-like or callable, optional
    Return a subset of the columns. If list-like, all elements must either
    be positional (i.e. integer indices into the document columns) or strings
    that correspond to column names provided either by the user in `names` or
    inferred from the document header row(s). For example, a valid list-like
    `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
    Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
    To instantiate a DataFrame from ``data`` with element order preserved use
    ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
    in ``['foo', 'bar']`` order or
    ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
    for ``['bar', 'foo']`` order.

    If callable, the callable function will be evaluated against the column
    names, returning names where the callable function evaluates to True. An
    example of a valid callable argument would be ``lambda x: x.upper() in
    ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
    parsing time and lower memory usage.
squeeze : bool, default False
    If the parsed data only contains one column then return a Series.
prefix : str, optional
    Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
mangle_dupe_cols : bool, default True
    Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
    'X'...'X'. Passing in False will cause data to be overwritten if there
    are duplicate names in the columns.
dtype : Type name or dict of column -> type, optional
    Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,
    'c': 'Int64'}
    Use `str` or `object` together with suitable `na_values` settings
    to preserve and not interpret dtype.
    If converters are specified, they will be applied INSTEAD
    of dtype conversion.
engine : {'c', 'python'}, optional
    Parser engine to use. The C engine is faster while the python engine is
    currently more feature-complete.
converters : dict, optional
    Dict of functions for converting values in certain columns. Keys can either
    be integers or column labels.
true_values : list, optional
    Values to consider as True.
false_values : list, optional
    Values to consider as False.
skipinitialspace : bool, default False
    Skip spaces after delimiter.
skiprows : list-like, int or callable, optional
    Line numbers to skip (0-indexed) or number of lines to skip (int)
    at the start of the file.

    If callable, the callable function will be evaluated against the row
    indices, returning True if the row should be skipped and False otherwise.
    An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
    Number of lines at bottom of file to skip (Unsupported with engine='c').
nrows : int, optional
    Number of rows of file to read. Useful for reading pieces of large files.
na_values : scalar, str, list-like, or dict, optional
    Additional strings to recognize as NA/NaN. If dict passed, specific
    per-column NA values.  By default the following values are interpreted as
    NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
    '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',
    'nan', 'null'.
keep_default_na : bool, default True
    Whether or not to include the default NaN values when parsing the data.
    Depending on whether `na_values` is passed in, the behavior is as follows:

    * If `keep_default_na` is True, and `na_values` are specified, `na_values`
      is appended to the default NaN values used for parsing.
    * If `keep_default_na` is True, and `na_values` are not specified, only
      the default NaN values are used for parsing.
    * If `keep_default_na` is False, and `na_values` are specified, only
      the NaN values specified `na_values` are used for parsing.
    * If `keep_default_na` is False, and `na_values` are not specified, no
      strings will be parsed as NaN.

    Note that if `na_filter` is passed in as False, the `keep_default_na` and
    `na_values` parameters will be ignored.
na_filter : bool, default True
    Detect missing value markers (empty strings and the value of na_values). In
    data without any NAs, passing na_filter=False can improve the performance
    of reading a large file.
verbose : bool, default False
    Indicate number of NA values placed in non-numeric columns.
skip_blank_lines : bool, default True
    If True, skip over blank lines rather than interpreting as NaN values.
parse_dates : bool or list of int or names or list of lists or dict, default False
    The behavior is as follows:

    * boolean. If True -> try parsing the index.
    * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
      each as a separate date column.
    * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
      a single date column.
    * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
      result 'foo'

    If a column or index cannot be represented as an array of datetimes,
    say because of an unparsable value or a mixture of timezones, the column
    or index will be returned unaltered as an object data type. For
    non-standard datetime parsing, use ``pd.to_datetime`` after
    ``pd.read_csv``. To parse an index or column with a mixture of timezones,
    specify ``date_parser`` to be a partially-applied
    :func:`pandas.to_datetime` with ``utc=True``. See
    :ref:`io.csv.mixed_timezones` for more.

    Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
    If True and `parse_dates` is enabled, pandas will attempt to infer the
    format of the datetime strings in the columns, and if it can be inferred,
    switch to a faster method of parsing them. In some cases this can increase
    the parsing speed by 5-10x.
keep_date_col : bool, default False
    If True and `parse_dates` specifies combining multiple columns then
    keep the original columns.
date_parser : function, optional
    Function to use for converting a sequence of string columns to an array of
    datetime instances. The default uses ``dateutil.parser.parser`` to do the
    conversion. Pandas will try to call `date_parser` in three different ways,
    advancing to the next if an exception occurs: 1) Pass one or more arrays
    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
    string values from the columns defined by `parse_dates` into a single array
    and pass that; and 3) call `date_parser` once for each row using one or
    more strings (corresponding to the columns defined by `parse_dates`) as
    arguments.
dayfirst : bool, default False
    DD/MM format dates, international and European format.
cache_dates : bool, default True
    If True, use a cache of unique, converted dates to apply the datetime
    conversion. May produce significant speed-up when parsing duplicate
    date strings, especially ones with timezone offsets.

    .. versionadded:: 0.25.0
iterator : bool, default False
    Return TextFileReader object for iteration or getting chunks with
    ``get_chunk()``.

    .. versionchanged:: 1.2

       ``TextFileReader`` is a context manager.
chunksize : int, optional
    Return TextFileReader object for iteration.
    See the `IO Tools docs
    <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
    for more information on ``iterator`` and ``chunksize``.

    .. versionchanged:: 1.2

       ``TextFileReader`` is a context manager.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
    For on-the-fly decompression of on-disk data. If 'infer' and
    `filepath_or_buffer` is path-like, then detect compression from the
    following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
    decompression). If using 'zip', the ZIP file must contain only one data
    file to be read in. Set to None for no decompression.
thousands : str, optional
    Thousands separator.
decimal : str, default '.'
    Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), optional
    Character to break file into lines. Only valid with C parser.
quotechar : str (length 1), optional
    The character used to denote the start and end of a quoted item. Quoted
    items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default 0
    Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
    QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
doublequote : bool, default ``True``
   When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
   whether or not to interpret two consecutive quotechar elements INSIDE a
   field as a single ``quotechar`` element.
escapechar : str (length 1), optional
    One-character string used to escape other characters.
comment : str, optional
    Indicates remainder of line should not be parsed. If found at the beginning
    of a line, the line will be ignored altogether. This parameter must be a
    single character. Like empty lines (as long as ``skip_blank_lines=True``),
    fully commented lines are ignored by the parameter `header` but not by
    `skiprows`. For example, if ``comment='#'``, parsing
    ``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
    treated as the header.
encoding : str, optional
    Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
    standard encodings
    <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
    .. versionchanged:: 1.2

       When ``encoding`` is ``None``, ``errors="replace"`` is passed to
       ``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``.
       This behavior was previously only the case for ``engine="python"``.
dialect : str or csv.Dialect, optional
    If provided, this parameter will override values (default or not) for the
    following parameters: `delimiter`, `doublequote`, `escapechar`,
    `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
    override values, a ParserWarning will be issued. See csv.Dialect
    documentation for more details.
error_bad_lines : bool, default True
    Lines with too many fields (e.g. a csv line with too many commas) will by
    default cause an exception to be raised, and no DataFrame will be returned.
    If False, then these "bad lines" will dropped from the DataFrame that is
    returned.
warn_bad_lines : bool, default True
    If error_bad_lines is False, and warn_bad_lines is True, a warning for each
    "bad line" will be output.
delim_whitespace : bool, default False
    Specifies whether or not whitespace (e.g. ``' '`` or ``'    '``) will be
    used as the sep. Equivalent to setting ``sep='\s+'``. If this option
    is set to True, nothing should be passed in for the ``delimiter``
    parameter.
low_memory : bool, default True
    Internally process the file in chunks, resulting in lower memory use
    while parsing, but possibly mixed type inference.  To ensure no mixed
    types either set False, or specify the type with the `dtype` parameter.
    Note that the entire file is read into a single DataFrame regardless,
    use the `chunksize` or `iterator` parameter to return the data in chunks.
    (Only valid with C parser).
memory_map : bool, default False
    If a filepath is provided for `filepath_or_buffer`, map the file object
    directly onto memory and access the data directly from there. Using this
    option can improve performance because there is no longer any I/O overhead.
float_precision : str, optional
    Specifies which converter the C engine should use for floating-point
    values. The options are ``None`` or 'high' for the ordinary converter,
    'legacy' for the original lower precision pandas converter, and
    'round_trip' for the round-trip converter.

    .. versionchanged:: 1.2

storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc., if using a URL that will
    be parsed by ``fsspec``, e.g., starting "s3://", "gcs://". An error
    will be raised if providing this argument with a non-fsspec URL.
    See the fsspec and backend storage implementation docs for the set of
    allowed keys and values.

    .. versionadded:: 1.2

Returns
-------
DataFrame or TextParser
    A comma-separated values (csv) file is returned as two-dimensional
    data structure with labeled axes.

See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.

Examples
--------
>>> pd.read_csv('data.csv')  # doctest: +SKIP
File:      ~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers.py
Type:      function


abs(1,2,3)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-169-c40167e470c4> in <module>
----> 1 abs(1,2,3)

TypeError: abs() takes exactly one argument (3 given)


def foo(n):
    if n < 10:
        return n
    else:
        return -n


foo(1)

1


foo(12)

-12


from foooo import my_foo


my_foo(12)

-12


def bar():
    """
    Description: This function ....
    """
    pass


bar()


def foo(n):
    if n < 10:
        return n
    else:
        return -n, -n+1


foo(21)

(-21, -20)


a, b = foo(21)

a

-21

b

-20


def foo(n):
    if n > 10:
        print(n)


a = foo(20)

20

a


def my_power(x):
    return x*x


# 按照位置传入参数
my_power(2)

4


# 按照名称传入参数
my_power(x=3)

9


my_power(y=3)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-188-a9c67ab2c9df> in <module>
----> 1 my_power(y=3)

TypeError: my_power() got an unexpected keyword argument 'y'


def my_power(x, n):
    return x**n


my_power(2, 3)

8


my_power(2)
# 此时原有的代码失效了，参数个数不对。

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-191-ce6bf99cb87f> in <module>
----> 1 my_power(2)
      2 # 此时原有的代码失效了，参数个数不对。

TypeError: my_power() missing 1 required positional argument: 'n'


def my_power(x, n=2):
    return x**n


my_power(2)

4


def enroll(name, gender, city='Tianjin', nation='China'):
    print('name: ', name)
    print('gender: ', gender)
    print('city: ', city)
    print('nation: ', nation)


enroll('Michael','Male')

name:  Michael
gender:  Male
city:  Tianjin
nation:  China


enroll('Michell', 'Female', 'Beijing')

name:  Michell
gender:  Female
city:  Beijing
nation:  China


def my_append(lst=[]):
    lst.append('asd')
    return lst


my_append([1,2,3])

[1, 2, 3, 'asd']


my_append(['a','b','c'])

['a', 'b', 'c', 'asd']


my_append()

['asd']


my_append()

['asd', 'asd']


my_append()

['asd', 'asd', 'asd']


def my_append(lst=None):
    if lst == None:
        lst = []
    lst.append('asd')
    return lst


my_append()

['asd']


my_append()

['asd']


my_append([1,2,3])

[1, 2, 3, 'asd']


def my_sum(lst):
    sum = 0
    for n in lst:
        sum = sum + n
    return sum


my_sum([1,2,3,4,5,])

15


def my_sum(*args): # 参数args接收到了一个tuple
    sum = 0
    for n in args:
        sum = sum + n
    return sum


my_sum(1,2,3,4,5,6)

21


my_sum()

0


# 如果已经有一个list，要传入一个可变参数的函数
a = [1,2,3]
my_sum(a[0],a[1],a[2])

6


my_sum(*a)

6


def person(name, age, **kwargs):
    print('name: ', name, 'age: ', age, 'other: ', kwargs)


person('asd',13, city='Tianjin', nation='China', adasd='asdsad')

name:  asd age:  13 other:  {'city': 'Tianjin', 'nation': 'China', 'adasd': 'asdsad'}


date_info = {'year': "2021", 'month': '01', 'day': '03'}
author_info = {'author': 'Ronald Coase', 'article': 'The Nature of Firms'}
filename = '{year}-{month}-{day}: {author}, {article}'.format(**date_info,**author_info)


filename

'2021-01-03: Ronald Coase, The Nature of Firms'


def person(name, age, *, city, nation): # 只接受city, nation作为关键字参数
    print('name: ', name, 'age: ', age, 
          'city: ', city, 'nation: ', nation)


person('michael',16, city='Tianjin', nation='China')

name:  michael age:  16 city:  Tianjin nation:  China


person('michael',16, city='Tianjin', Nation='China')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-220-ce0684fdca7e> in <module>
----> 1 person('michael',16, city='Tianjin', Nation='China')

TypeError: person() got an unexpected keyword argument 'Nation'


def person(name, age, *args, city, nation):
    print('name: ', name, 'age: ', age, 
           *args,
          'city: ', city, 'nation: ', nation)


person('Jack', 15, 'Earth', 'Sun','universe', city='Beijing', nation='China',asdasd='asdasda')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-222-810a8e4e6e9c> in <module>
----> 1 person('Jack', 15, 'Earth', 'Sun','universe', city='Beijing', nation='China',asdasd='asdasda')

TypeError: person() got an unexpected keyword argument 'asdasd'


person('Jack', 15, 'Earth', 'Sun','Beijing','China')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-223-98fce01515b6> in <module>
----> 1 person('Jack', 15, 'Earth', 'Sun','Beijing','China')

TypeError: person() missing 2 required keyword-only arguments: 'city' and 'nation'


person('Jack', 15, 'Earth', 'Sun','Beijing','China',city='Beijing', nation='China')

name:  Jack age:  15 Earth Sun Beijing China city:  Beijing nation:  China


def foo(*, city, nation):
    print(city)
    print(nation)


foo('Beijing','China')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-226-c0dfdd778075> in <module>
----> 1 foo('Beijing','China')

TypeError: foo() takes 0 positional arguments but 2 were given


foo(city='Beijing',nation='China')

Beijing
China


def foo(city, nation):
    print(city)
    print(nation)


foo('Beijing','China')

Beijing
China


def f1(a, b, c=0, *args, **kw):
    pass


def fact(n):
    if n == 1: 
        return 1
    return n * fact(n-1)


fact(1)

1


fact(5)

120


for letter in 'asbdasdas':
    print(letter)

a
s
b
d
a
s
d
a
s


d = {'a':1, 'b':123, 'c':'asd'}


for i in d:
    print(i)

a
b
c


for value in d.values():
    print(value)

1
123
asd


d.items()

dict_items([('a', 1), ('b', 123), ('c', 'asd')])


for key, value in d.items():
    print(key, value)

a 1
b 123
c asd


list_ = [1,2,3,4,1,23,]


for e in list_:
    print(e)

1
2
3
4
1
23


list(enumerate(list_))

[(0, 1), (1, 2), (2, 3), (3, 4), (4, 1), (5, 23)]


for i, value in enumerate(list_):
    print(i, value)

0 1
1 2
2 3
3 4
4 1
5 23


l = iter(list_)


next(l)

1


next(l)

2


for e in l:
    print(e)

3
4
1
23


next(l)

---------------------------------------------------------------------------
StopIteration                             Traceback (most recent call last)
<ipython-input-248-cdc8a39da60d> in <module>
----> 1 next(l)

StopIteration:


l = iter(list_)


iter(l) # iter(iterator) 返回自身

<list_iterator at 0x7fbac5cb4610>

l

<list_iterator at 0x7fbac5cb4610>


l = []
for x in range(10):
    l.append(x**2)
l

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]


[x**2 for x in range(10)]

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]


[x**2 for x in range(10) if x%2==0]

[0, 4, 16, 36, 64]


ss = ['a','b','c']


[0,1,2]

[0, 1, 2]


'a'+'b'

'ab'


[s+f'_{n}' for s in ss for n in range(3)]

['a_0', 'a_1', 'a_2', 'b_0', 'b_1', 'b_2', 'c_0', 'c_1', 'c_2']


# 等价于
l_ = []
for s in ss:
    for n in range(3):
        l_.append(s+f'_{n}')

l_

['a_0', 'a_1', 'a_2', 'b_0', 'b_1', 'b_2', 'c_0', 'c_1', 'c_2']


[s+f'_{n+1}' for n,s in enumerate(ss)]

['a_1', 'b_2', 'c_3']

ss

['a', 'b', 'c']


?zip

Init signature: zip(self, /, *args, **kwargs)
Docstring:     
zip(*iterables) --> A zip object yielding tuples until an input is exhausted.

   >>> list(zip('abcdefg', range(3), range(4)))
   [('a', 0, 0), ('b', 1, 1), ('c', 2, 2)]

The zip object yields n-length tuples, where n is the number of iterables
passed as positional arguments to zip().  The i-th element in every tuple
comes from the i-th iterable argument to zip().  This continues until the
shortest argument is exhausted.
Type:           type
Subclasses:


zip(ss,range(3))

<zip at 0x7fbab9c23f40>


list(zip(ss,range(3)))

[('a', 0), ('b', 1), ('c', 2)]


list(zip(('a', 0), ('b', 1), ('c', 2)))

[('a', 'b', 'c'), (0, 1, 2)]


list(zip(*zip(ss,range(3))))

[('a', 'b', 'c'), (0, 1, 2)]


(x for x in range(10))

<generator object <genexpr> at 0x7fbab9c295f0>


g = (x for x in range(10))


g[0]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-270-73ec0726ff88> in <module>
----> 1 g[0]

TypeError: 'generator' object is not subscriptable


next(g)

0


next(g)

1


next(g)

2


for x in g:
    print(x)


def fib(max):
    n, a, b = 0, 0, 1
    while n < max:
        print(b)
        a, b = b, a+b # 等价于 a, b = (b, a+b)
                      # 或者 t = (b, a+b)
                      #     a = t[0]
                      #     b = t[1]
        n = n + 1
    return 'done'


fib(10)

1
1
2
3
5
8
13
21
34
55

'done'


def fib(max):
    n, a, b = 0, 0, 1
    while n < max:
        yield b
        a, b = b, a+b 
        n = n + 1
    return 'done'


f = fib(10)


next(f)

1


next(f)

1


for num in f:
    print(num)

2
3
5
8
13
21
34
55


next(range(10))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-282-a35518fc9185> in <module>
----> 1 next(range(10))

TypeError: 'range' object is not an iterator


abs(-2)

2

abs

<function abs(x, /)>


display(abs)

<function abs(x, /)>


print(abs)

<built-in function abs>


foo = abs


foo(-2)

2


# abs = 'abs' # 可以但最好不要这么做


abs(2)

2


def foo(x1,x2,func):
    return func(x1) + func(x2)


foo(1,2,abs)

3


foo(-1,4,abs)

5


import numpy as np


foo(123,14,np.log)

7.451241684987675


map(abs, [1,2,-3,-5])

<map at 0x7fbab9c03400>


m = map(abs, [1,2,-3,-5])


list(m)

[1, 2, 3, 5]


from collections.abc import Iterable


isinstance(m, Iterable)

True


list(m)

[]


from functools import reduce


def foo(x,y):
    return x + y


reduce(foo,[1,2,3,4,5])

15


def is_odd(x):
    return x % 2 == 1


filter(is_odd, [1,2,3,4,5,6,7,9])

<filter at 0x7fbac5cb4130>


list(filter(is_odd, [1,2,3,4,5,6,7,9]))

[1, 3, 5, 7, 9]


lambda x: x**3

<function __main__.<lambda>(x)>


f = lambda x: x**3
f(2)

8


f = lambda x,y: x*10-y
f(3,1)

29


d = [('a', 100), ('b', 23), ('c', 1890)]


?sorted

Signature: sorted(iterable, /, *, key=None, reverse=False)
Docstring:
Return a new list containing all items from the iterable in ascending order.

A custom key function can be supplied to customize the sort order, and the
reverse flag can be set to request the result in descending order.
Type:      builtin_function_or_method


sorted(d, key=lambda x: x[1]) #按照dict里面的value进行排序

[('b', 23), ('a', 100), ('c', 1890)]


my_add = lambda x: lambda y: x+y


my_add(4)(5)

9


def log(func):
    def wrapper(*args, **kw):
        print('call %s():' % func.__name__)
        return func(*args, **kw)
    return wrapper


log(print)

<function __main__.log.<locals>.wrapper(*args, **kw)>


wrapper()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-318-4cb166442f3c> in <module>
----> 1 wrapper()

NameError: name 'wrapper' is not defined


log(print)('hello')

call print():
hello


def cat_miao():
    print('miao miao')


log(cat_miao)()

call cat_miao():
miao miao


@log
def cat_miao():
    print('miao miao')


cat_miao()

call cat_miao():
miao miao


def print_func_name(func):
    def wrap_1():
        print("Now use function '{}'".format(func.__name__))
        func()
    return wrap_1


def print_time(func):
    import time
    def wrap_2():
        print("Now the time is {}".format(int(time.time())))
        func()
    return wrap_2


@print_func_name
@print_time
def dog_bark():
    print("Bark !!!")


dog_bark()

Now use function 'wrap_2'
Now the time is 1644892343
Bark !!!


def log(text):
    def decorator(func):
        def wrapper(*args, **kwargs):
            print('{} {}(): '.format(text, func.__name__))
            return func(*args, **kwargs)
        return wrapper
    return decorator


@log('execute')
def cat_miao():
    print('miao miao')


cat_miao()

execute cat_miao(): 
miao miao


cat_miao.__name__

'wrapper'


import functools

def log(text):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            print('{} {}(): '.format(text, func.__name__))
            return func(*args, **kwargs)
        return wrapper
    return decorator


@log('execute')
def cat_miao():
    print('miao miao')


cat_miao.__name__

'cat_miao'


def log(text):
    def decorator(func):
        def wrapper(*args, **kwargs):
            print('{} {}(): '.format(text, func.__name__))
            return func(*args, **kwargs)
        return wrapper
    return decorator


def my_height():
    height = 10
    def print_height():
        print("The height is: %d" % height)
    return print_height


h = my_height()

h

<function __main__.my_height.<locals>.print_height()>

h()

The height is: 10


int('12314')

12314


int('0101010101111', base=2)

2735


import functools
int2 = functools.partial(int, base=2)


int2('100001101001')

2153


int2('100001101001', base=8)

8590230017


import my_package


my_package.xyz.my_print()

 Roses are red,
 Violets are blue,
 Whatever you say,
 is always true.


import my_package.xyz as xyz


xyz.my_print()

 Roses are red,
 Violets are blue,
 Whatever you say,
 is always true.


xyz.my_print()

 Roses are red,
 Violets are blue,
 Whatever you say,
 is always true.


from importlib import reload


reload(my_package)

<module 'my_package' from '/Users/Bert/Google Drive/03-Learning/teaching/quant/lec_notes/lec1/2022/my_package/__init__.py'>


my_package.xyz.my_print()

 Roses are red,
 Violets are blue,
 Whatever you say,
 is always true.


reload(my_package.xyz)

<module 'my_package.xyz' from '/Users/Bert/Google Drive/03-Learning/teaching/quant/lec_notes/lec1/2022/my_package/xyz.py'>


xyz.my_print()

 Roses are red,
 Violets are blue,
 Whatever you say,
 is always true.


def _print1(x):
    return print(f'{x}')

def _print2(x):
    return print(f'{x} + {x}')

def hello(num):
    if num > 3:
        return _print1(num)
    else:
        return _print2(num)


class Student(object):
    pass
# class Student():
#     pass
# class Student:
#     pass


Student

__main__.Student


print(Student)

<class '__main__.Student'>


xiaoming = Student()


xiaoming

<__main__.Student at 0x7fbac5cb40a0>


xiaoming.name = 'xiaoming'


xiaoming.sex = 'male'


xiaoming.name

'xiaoming'


class Student(object):
    class_attr = 'class 8'
    
    def __init__(self, name, score):
        self.name = name
        self.score = score


xm = Student()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-363-66870c6d0e5c> in <module>
----> 1 xm = Student()

TypeError: __init__() missing 2 required positional arguments: 'name' and 'score'


xm = Student(name='xiaoming',score=70)


xm.name

'xiaoming'


xm.score

70


xm.class_attr

'class 8'


xf = Student(name='xiaofang',score=90)


xf.class_attr

'class 8'


xm.name = 'xiaomingming'


xm.name

'xiaomingming'


print(xm.score)

70


class Student(object):

    def __init__(self, name, score):
        self.name = name
        self.score = score

    def print_score(self):
        print('%s: %s' % (self.name, self.score))


xm = Student('xiaoming',70)


xm.print_score()

xiaoming: 70


class Student(object):

    def __init__(self, name, score):
        self.name = name
        self.score = score

    def print_score(self):
        print('%s: %s' % (self.name, self.score))
    
    def get_grade(self):
        if self.score >= 90:
            return 'A'
        elif self.score >= 60:
            return 'B'
        else:
            return 'C'


xm = Student('xiaoming',70)


xm.get_grade()

'B'


class Student(object):

    def __init__(self, name, score):
        self.__name = name
        self.__score = score

    def print_score(self):
        print('%s: %s' % (self.__name, self.__score))
    
    def get_grade(self):
        if self.__score >= 90:
            return 'A'
        elif self.__score >= 60:
            return 'B'
        else:
            return 'C'


xm = Student('xiaoming', 80)


xm.__name

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-381-1acdeb6bb54c> in <module>
----> 1 xm.__name

AttributeError: 'Student' object has no attribute '__name'


xm.get_grade()

'B'


class Student(object):

    def __init__(self, name, score):
        self.__name = name
        self.__score = score

    def print_score(self):
        print('%s: %s' % (self.__name, self.__score))
    
    def get_grade(self):
        if self.__score >= 90:
            return 'A'
        elif self.__score >= 60:
            return 'B'
        else:
            return 'C'
    def get_name(self):
        return self.__name
    
    def get_score(self):
        return self.__score


xm = Student('xiaoming',70)


xm.__name

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-385-1acdeb6bb54c> in <module>
----> 1 xm.__name

AttributeError: 'Student' object has no attribute '__name'


xm.get_name()

'xiaoming'


class Student(object):

    def __init__(self, name, score):
        self.__name = name
        self.__score = score

    def print_score(self):
        print('%s: %s' % (self.__name, self.__score))
    
    def get_grade(self):
        if self.__score >= 90:
            return 'A'
        elif self.__score >= 60:
            return 'B'
        else:
            return 'C'
    def get_name(self):
        return self.__name
    
    def get_score(self):
        return self.__score
    
    def set_score(self, score):
        self.__score = score


class Student(object):

    def __init__(self, name, score):
        self.__name = name
        self.__score = score

    def print_score(self):
        print('%s: %s' % (self.__name, self.__score))
    
    def get_grade(self):
        if self.__score >= 90:
            return 'A'
        elif self.__score >= 60:
            return 'B'
        else:
            return 'C'
    def get_name(self):
        return self.__name
    
    def get_score(self):
        return self.__score
    
    def set_score(self, score):
        if 0 <= score <= 100:
            self.__score = score
        else:
            raise ValueError('score must be between 0 and 100.')


xm = Student('xm', 70)


xm.set_score(199)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-390-946feb87f11b> in <module>
----> 1 xm.set_score(199)

<ipython-input-388-0e5e7e5cb2cb> in set_score(self, score)
     25             self.__score = score
     26         else:
---> 27             raise ValueError('score must be between 0 and 100.')

ValueError: score must be between 0 and 100.


xm._Student__name

'xm'


xm._Student__name = 'haha' # 实际上可以改


xm.get_name()

'haha'


del xm._Student__name # 甚至删除


xm._Student__name

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-395-ab9abfca7ae9> in <module>
----> 1 xm._Student__name

AttributeError: 'Student' object has no attribute '_Student__name'


xm.__name = 'heyhey'


xm.get_name()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-397-596696feed22> in <module>
----> 1 xm.get_name()

<ipython-input-388-0e5e7e5cb2cb> in get_name(self)
     16             return 'C'
     17     def get_name(self):
---> 18         return self.__name
     19 
     20     def get_score(self):

AttributeError: 'Student' object has no attribute '_Student__name'


class Animal(object):
    
    def __init__(self, legs=4, fur=True):
        self.legs = legs
        self.fur = fur
    
    def run(self):
        print('Animal is running...')


class Dog(Animal):

    pass

class Cat(Animal):
    pass


dog = Dog()
cat = Cat()


dog.run()

Animal is running...


dog.legs

4


cat.fur

True


class Dog(Animal):
    def eat(self):
        print('Eating meat...')


dog = Dog()
dog.eat()

Eating meat...


class Dog(Animal):
    def eat(self):
        print('Eating meat...')
    def run(self):
        print('dog is running...')


class Cat(Animal):

    def run(self):
        print('cat is running...')


dog = Dog()
dog.run()

dog is running...


a = list()
b = Animal()
c = Dog()
isinstance(a, list)

True


isinstance(b, Animal)

True


isinstance(c, Dog)

True


isinstance(c, Animal)

True


isinstance(b, Dog)

False


def run_twice(animal):
    animal.run()
    animal.run()


run_twice(Animal())

Animal is running...
Animal is running...


run_twice(Dog())

dog is running...
dog is running...


run_twice(Cat())

cat is running...
cat is running...


a = [1,2,3]
a.__len__()

3


# 定义一个新的子类
class Tortoise(Animal):
    def run(self):
        print('Tortoise is running slowly...')


run_twice(Tortoise())

Tortoise is running slowly...
Tortoise is running slowly...


class DuckTyping(object):
    def run(self):
        print('This is a duck typing')


run_twice(DuckTyping())

This is a duck typing
This is a duck typing


print(type(10))

<class 'int'>


print(type('str'))

<class 'str'>


print(type(Animal()))

<class '__main__.Animal'>


print(type(Dog()))

<class '__main__.Dog'>


print(type(abs))

<class 'builtin_function_or_method'>


import types


type(run_twice) == types.FunctionType

True


type(abs) == types.BuiltinFunctionType

True


type(abs) == types.FunctionType

False


type(lambda x:x) == types.LambdaType

True


type((x for x in range(10))) == types.GeneratorType

True


isinstance(c, Animal)

True


isinstance(c, Dog)

True


isinstance(c, (Dog, str))

True


isinstance(123, int)

True


dir(types)

['AsyncGeneratorType',
 'BuiltinFunctionType',
 'BuiltinMethodType',
 'CellType',
 'ClassMethodDescriptorType',
 'CodeType',
 'CoroutineType',
 'DynamicClassAttribute',
 'FrameType',
 'FunctionType',
 'GeneratorType',
 'GenericAlias',
 'GetSetDescriptorType',
 'LambdaType',
 'MappingProxyType',
 'MemberDescriptorType',
 'MethodDescriptorType',
 'MethodType',
 'MethodWrapperType',
 'ModuleType',
 'SimpleNamespace',
 'TracebackType',
 'WrapperDescriptorType',
 '_GeneratorWrapper',
 '__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__spec__',
 '_calculate_meta',
 '_cell_factory',
 'coroutine',
 'new_class',
 'prepare_class',
 'resolve_bases']


dir(Animal)

['__class__',
 '__delattr__',
 '__dict__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__gt__',
 '__hash__',
 '__init__',
 '__init_subclass__',
 '__le__',
 '__lt__',
 '__module__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__setattr__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 '__weakref__',
 'run']


'a' + 'b'

'ab'


'a'.__add__('b')

'ab'


dir('abv')

['__add__',
 '__class__',
 '__contains__',
 '__delattr__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__getitem__',
 '__getnewargs__',
 '__gt__',
 '__hash__',
 '__init__',
 '__init_subclass__',
 '__iter__',
 '__le__',
 '__len__',
 '__lt__',
 '__mod__',
 '__mul__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__rmod__',
 '__rmul__',
 '__setattr__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 'capitalize',
 'casefold',
 'center',
 'count',
 'encode',
 'endswith',
 'expandtabs',
 'find',
 'format',
 'format_map',
 'index',
 'isalnum',
 'isalpha',
 'isascii',
 'isdecimal',
 'isdigit',
 'isidentifier',
 'islower',
 'isnumeric',
 'isprintable',
 'isspace',
 'istitle',
 'isupper',
 'join',
 'ljust',
 'lower',
 'lstrip',
 'maketrans',
 'partition',
 'removeprefix',
 'removesuffix',
 'replace',
 'rfind',
 'rindex',
 'rjust',
 'rpartition',
 'rsplit',
 'rstrip',
 'split',
 'splitlines',
 'startswith',
 'strip',
 'swapcase',
 'title',
 'translate',
 'upper',
 'zfill']


len('abc')

3


df = pd.DataFrame([[1,2,3,4,5],[5,4,3,2,1]])
df


len(df)

2


class MyHair(object):
    def __len__(self):
        return 100


len(MyHair())

100


dog = Dog()
hasattr(dog, 'legs')

True


hasattr(dog, 'tail')

False


setattr(dog, 'fur', 'yes')


dog.fur

'yes'


getattr(dog, 'fur')

'yes'


getattr(dog, 'tail', 'No tail')

'No tail'


hasattr(dog, 'run')

True


foo = getattr(dog, 'run')

foo

<bound method Dog.run of <__main__.Dog object at 0x7fbab9c7bfd0>>


foo() # 等价于 dog.run()

dog is running...


try:
    print('try...')
    r = 10 / 0
    print('result:', r)
except ZeroDivisionError as e:
    print('except:', e)
finally:
    print('finally...')
print('END')

try...
except: division by zero
finally...
END


try:
    print('try...')
    r = 10 / 3
    print('result:', r)
except ZeroDivisionError as e:
    print('except:', e)
finally:
    print('finally...')
print('END')

try...
result: 3.3333333333333335
finally...
END


import math

def my_sqrt(x):
    print(math.sqrt(x))

try:
    my_sqrt(-2)
except ValueError as e:
    print('ValueError')
except UnicodeError as e:
    print('UnicodeError')

ValueError


try:
    print('try...')
    r = 10 / int('2')
    print('result:', r)
except ValueError as e:
    print('ValueError:', e)
except ZeroDivisionError as e:
    print('ZeroDivisionError:', e)
else:
    print('no error!')
finally:
    print('finally...')
print('END')

try...
result: 5.0
no error!
finally...
END


def foo(s):
    return 10 / int(s)

def bar(s):
    return foo(s) * 2

def main():
    bar('0')

main()

---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-462-189c120ae7d8> in <module>
      8     bar('0')
      9 
---> 10 main()

<ipython-input-462-189c120ae7d8> in main()
      6 
      7 def main():
----> 8     bar('0')
      9 
     10 main()

<ipython-input-462-189c120ae7d8> in bar(s)
      3 
      4 def bar(s):
----> 5     return foo(s) * 2
      6 
      7 def main():

<ipython-input-462-189c120ae7d8> in foo(s)
      1 def foo(s):
----> 2     return 10 / int(s)
      3 
      4 def bar(s):
      5     return foo(s) * 2

ZeroDivisionError: division by zero

字符	ASCII	Unicode	UTF-8
A	01000001	00000000 01000001	01000001
中	x	01001110 00101101	11100100 10111000 10101101

	Stkcd	Stknmec	Updt	Conme	Conmee	IndClaCd	Indus	Indnme	Listdt	Udwnm	Sponsor	Http
0	֤ȯ��	֤ȯ��	��	��˾��	��˾Ӣ��	��ҵ��׼	��ҵ��루�£�	��ҵ��	�״��	��	��Ƽ��	��˾��ʻ��ַ
1	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ	û�е�λ
2	000001	ƽ��	2018-12-24	ƽ��йɷ��޹�˾	Ping An Bank Co., Ltd.	2	J66	��ҽ��ڷ��	1991-04-03	��֤ȯ��˾	��ھ��֤ȯ��˾	www.bank.pingan.com
3	000002	��A	2018-12-24	��ҵ�ɷ��޹�˾	China Vanke Co., Ltd.	2	K70	��ز�ҵ	1991-01-29	��ھ��֤ȯ��˾	��ھ��֤ȯ��˾	www.vanke.com
4	000003	PT ��A	2002-06-14	��ʵҵ(��)�ɷ��޹�˾	Gintian Industry (Group) Co., Ltd.	1	M	�ۺ��	1991-07-03	��֤ȯ��˾	��ھ��֤ȯ��˾	www.gintiangroup.com
...	...	...	...	...	...	...	...	...	...	...	...	...
3801	900952	��B��	2018-12-24	��ݸ۹ɷ��޹�˾	Jinzhou Port Co., Ltd.	2	G55	ˮ��ҵ	1998-05-19	�㷢֤ȯ��ι�˾	�㷢֤ȯ��ι�˾	www.jinzhouport.com
3802	900953	��B	2018-12-24	��쿭��ɷ��޹�˾	Kama Co., Ltd.	2	C36	��ҵ	1998-06-24	��֤ȯ��޹�˾	��֤ȯ��޹�˾	www.kama.com.cn
3803	900955	��B��	2018-12-26	��¹ɷ��޹�˾	HNA INNOVATION CO.,LTD.	2	K70	��ز�ҵ	1999-01-18	��֤ȯ�ɷ��޹�˾	��֤ȯ�ɷ��޹�˾	www.ninedragon.com.cn,www.hnainnovation.com
3804	900956	��B��	2018-12-24	��ʯ��ɷ��޹�˾	Huangshi Dongbei Electrical Appliance Co., Ltd.	2	C34	ͨ��豸��ҵ	1999-07-15	��֤ͨȯ��ι�˾	��֤ͨȯ��ι�˾	www.donper.com
3805	900957	��B��	2018-12-24	�Ϻ��ʵҵ��չ�ɷ��޹�˾	Shanghai Lingyun Industries Development Co., Ltd.	2	K70	��ز�ҵ	2000-07-28	��̩��֤ȯ�ɷ��޹�˾	��̩��֤ȯ�ɷ��޹�˾	www.elingyun.com

	Stkcd	Stknmec	Updt	Conme	Conmee	IndClaCd	Indus	Indnme	Listdt	Udwnm	Sponsor	Http
0	证券代码	证券简称	更新日期	公司中文名称	公司英文名称	行业分类标准	行业代码（新）	行业名称	首次上市日期	主承销商	上市推荐人	公司国际互联网址
1	没有单位	没有单位	没有单位	没有单位	没有单位	没有单位	没有单位	没有单位	没有单位	没有单位	没有单位	没有单位
2	000001	平安银行	2018-12-24	平安银行股份有限公司	Ping An Bank Co., Ltd.	2	J66	货币金融服务	1991-04-03	深圳特区证券公司	深圳经济特区证券公司	www.bank.pingan.com
3	000002	万科A	2018-12-24	万科企业股份有限公司	China Vanke Co., Ltd.	2	K70	房地产业	1991-01-29	深圳经济特区证券公司	深圳经济特区证券公司	www.vanke.com
4	000003	PT 金田A	2002-06-14	金田实业(集团)股份有限公司	Gintian Industry (Group) Co., Ltd.	1	M	综合类	1991-07-03	深圳特区证券公司	深圳经济特区证券公司	www.gintiangroup.com
...	...	...	...	...	...	...	...	...	...	...	...	...
3801	900952	锦港B股	2018-12-24	锦州港股份有限公司	Jinzhou Port Co., Ltd.	2	G55	水上运输业	1998-05-19	广发证券有限责任公司	广发证券有限责任公司	www.jinzhouport.com
3802	900953	凯马B	2018-12-24	恒天凯马股份有限公司	Kama Co., Ltd.	2	C36	汽车制造业	1998-06-24	华夏证券有限公司	华夏证券有限公司	www.kama.com.cn
3803	900955	海创B股	2018-12-26	海航创新股份有限公司	HNA INNOVATION CO.,LTD.	2	K70	房地产业	1999-01-18	申银万国证券股份有限公司	申银万国证券股份有限公司	www.ninedragon.com.cn,www.hnainnovation.com
3804	900956	东贝B股	2018-12-24	黄石东贝电器股份有限公司	Huangshi Dongbei Electrical Appliance Co., Ltd.	2	C34	通用设备制造业	1999-07-15	国通证券有限责任公司	国通证券有限责任公司	www.donper.com
3805	900957	凌云B股	2018-12-24	上海凌云实业发展股份有限公司	Shanghai Lingyun Industries Development Co., Ltd.	2	K70	房地产业	2000-07-28	国泰君安证券股份有限公司	国泰君安证券股份有限公司	www.elingyun.com

	secID	secShortName	exchangeCD	tradeDate	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
0	000001.XSHE	平安银行	XSHE	2015-01-05	1293.044	1307.737	4966040	4.565388e+09	92478.0	0.0291	1.575841e+11	1.830268e+11
1	000001.XSHE	平安银行	XSHE	2015-01-06	1307.737	1288.146	3761152	3.453446e+09	80325.0	0.0220	1.552233e+11	1.802848e+11
2	000001.XSHE	平安银行	XSHE	2015-01-07	1288.146	1263.656	2951601	2.634796e+09	72697.0	0.0173	1.522723e+11	1.768574e+11
3	000001.XSHE	平安银行	XSHE	2015-01-08	1263.656	1221.208	2443951	2.128003e+09	68734.0	0.0143	1.471572e+11	1.709164e+11
4	000001.XSHE	平安银行	XSHE	2015-01-09	1221.208	1231.004	4355039	3.835378e+09	99882.0	0.0255	1.483376e+11	1.722874e+11
...	...	...	...	...	...	...	...	...	...	...	...	...
5838745	900957.XSHG	凌云B股	XSHG	2021-12-27	0.625	0.638	488511	3.081540e+05	226.0	0.0027	1.164720e+08	2.209170e+08
5838746	900957.XSHG	凌云B股	XSHG	2021-12-28	0.638	0.637	177702	1.118810e+05	64.0	0.0010	1.162880e+08	2.205680e+08
5838747	900957.XSHG	凌云B股	XSHG	2021-12-29	0.637	0.630	123550	7.733300e+04	58.0	0.0007	1.150000e+08	2.181250e+08
5838748	900957.XSHG	凌云B股	XSHG	2021-12-30	0.630	0.635	113600	7.130800e+04	41.0	0.0006	1.159200e+08	2.198700e+08
5838749	900957.XSHG	凌云B股	XSHG	2021-12-31	0.635	0.636	167800	1.059960e+05	74.0	0.0009	1.161040e+08	2.202190e+08

	secID	secShortName	exchangeCD	tradeDate	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
0	000001.XSHE	平安银行	XSHE	2015-01-05	1293.044	1307.737	4966040	4.565388e+09	92478.0	0.0291	1.575841e+11	1.830268e+11
1	000001.XSHE	平安银行	XSHE	2015-01-06	1307.737	1288.146	3761152	3.453446e+09	80325.0	0.0220	1.552233e+11	1.802848e+11
2	000001.XSHE	平安银行	XSHE	2015-01-07	1288.146	1263.656	2951601	2.634796e+09	72697.0	0.0173	1.522723e+11	1.768574e+11
3	000001.XSHE	平安银行	XSHE	2015-01-08	1263.656	1221.208	2443951	2.128003e+09	68734.0	0.0143	1.471572e+11	1.709164e+11

Python 简介¶

安装Python¶

常用工具介绍¶

模块安装¶

环境¶

Python 基础¶

数据类型和变量¶

整数¶

浮点数¶

字符串¶

字符串中引用变量¶

关于编码¶

中国数据的例子¶

布尔值¶

& 是bit-wise的操作¶

空值¶

变量¶

常量¶

List, tuple¶

List¶

tuple¶

条件判断 if, else¶

循环 Loops¶

dict, set¶

不变的、可变的对象，要注意区分¶

函数¶

自定义函数¶

pass¶

返回多个值¶

参数¶

默认参数¶

可变数目的参数¶

可变数目的关键字参数¶

递归函数¶

List Comprehension, Iterable, Iterator, Lazy Iterable, Generator¶

Iterable¶

Iterator¶

List comprehension¶

zip 函数¶

Generator¶

和函数相关的一些其他用法¶

map,reduce,filter¶

map¶

reduce¶

filter¶

lambda 函数¶

decorator¶

多个 decorator 的顺序¶

若 decorator 本身需要参数¶

closure¶

Partial function¶

模块 Module¶

作用域¶

Object Oriented Programming (OOP) 面向对象编程¶

封装¶

访问限制¶

继承和多态, inheritance and ploymorphism¶

多态¶

duck typing¶

获取对象信息¶

type()¶

isinstance()¶

dir()¶

错误处理¶

try...except...finally¶

参考资料¶

`pass`¶

`zip` 函数¶