稀牛学院第1课笔记

作者: Bog5d | 来源:发表于2017-11-25 21:56 被阅读262次

稀牛lesson1

python 基础

python 简介

python R
一天内能写Python是有可能的
流行程度：深度学习，人工智能

深度学习

google：tensorflow

facebook：pytorch+caffe2

Amazone:mxnet

机器学习

scikit-learn numpy pandas xgboost/LightGBM

大数据

hadoop（map-reduce） JAVA spark scala

都是用Python去实现的

因为数据科学家，用数据驱动的方式去解决实际生活中的各种问题。但是，花了大量时间去coding
但理想的状况是，你把主要的精力放在分析数据，了解数据和解决问题本身上

1. 寻求帮助

Python有内置的说明文档 help
dir

import pandas as pd 
# 数据科学的比赛，阿里，京东，会有比赛，其实可以多去参加的

help(pd)

Help on package pandas:

NAME
    pandas

DESCRIPTION
    pandas - a powerful data analysis and manipulation library for Python
    =====================================================================
    
    **pandas** is a Python package providing fast, flexible, and expressive data
    structures designed to make working with "relational" or "labeled" data both
    easy and intuitive. It aims to be the fundamental high-level building block for
    doing practical, **real world** data analysis in Python. Additionally, it has
    the broader goal of becoming **the most powerful and flexible open source data
    analysis / manipulation tool available in any language**. It is already well on
    its way toward this goal.
    
    Main Features
    -------------
    Here are just a few of the things that pandas does well:
    
      - Easy handling of missing data in floating point as well as non-floating
        point data
      - Size mutability: columns can be inserted and deleted from DataFrame and
        higher dimensional objects
      - Automatic and explicit data alignment: objects can  be explicitly aligned
        to a set of labels, or the user can simply ignore the labels and let
        `Series`, `DataFrame`, etc. automatically align the data for you in
        computations
      - Powerful, flexible group by functionality to perform split-apply-combine
        operations on data sets, for both aggregating and transforming data
      - Make it easy to convert ragged, differently-indexed data in other Python
        and NumPy data structures into DataFrame objects
      - Intelligent label-based slicing, fancy indexing, and subsetting of large
        data sets
      - Intuitive merging and joining data sets
      - Flexible reshaping and pivoting of data sets
      - Hierarchical labeling of axes (possible to have multiple labels per tick)
      - Robust IO tools for loading data from flat files (CSV and delimited),
        Excel files, databases, and saving/loading data from the ultrafast HDF5
        format
      - Time series-specific functionality: date range generation and frequency
        conversion, moving window statistics, moving window linear regressions,
        date shifting and lagging, etc.

PACKAGE CONTENTS
    _libs (package)
    _version
    api (package)
    compat (package)
    computation (package)
    conftest
    core (package)
    errors (package)
    formats (package)
    io (package)
    json
    lib
    parser
    plotting (package)
    stats (package)
    testing
    tests (package)
    tools (package)
    tseries (package)
    tslib
    types (package)
    util (package)

SUBMODULES
    _hashtable
    _lib
    _tslib
    offsets

DATA
    IndexSlice = <pandas.core.indexing._IndexSlice object>
    NaT = NaT
    __docformat__ = 'restructuredtext'
    datetools = <module 'pandas.core.datetools' from '/home/bog/...ython3....
    describe_option = <pandas.core.config.CallableDynamicDoc object>
    get_option = <pandas.core.config.CallableDynamicDoc object>
    json = <module 'pandas.json' from '/home/bog/anaconda3/lib/python3.6/s...
    lib = <module 'pandas.lib' from '/home/bog/anaconda3/lib/python3.6/sit...
    options = <pandas.core.config.DictWrapper object>
    parser = <module 'pandas.parser' from '/home/bog/anaconda3/lib/python3...
    plot_params = {'xaxis.compat': False}
    reset_option = <pandas.core.config.CallableDynamicDoc object>
    set_option = <pandas.core.config.CallableDynamicDoc object>
    tslib = <module 'pandas.tslib' from '/home/bog/anaconda3/lib/python3.6...

VERSION
    0.20.3

FILE
    /home/bog/anaconda3/lib/python3.6/site-packages/pandas/__init__.py

import tensorflow as tf

help(tf)

Help on package tensorflow:

NAME
    tensorflow

DESCRIPTION
    # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================

PACKAGE CONTENTS
    contrib (package)
    core (package)
    examples (package)
    python (package)
    tensorboard (package)
    tools (package)

SUBMODULES
    app
    compat
    errors
    estimator
    flags
    gfile
    graph_util
    image
    layers
    logging
    losses
    metrics
    nn
    python_io
    pywrap_tensorflow
    resource_loader
    saved_model
    sdca
    sets
    spectral
    summary
    sysconfig
    test
    train
    user_ops

DATA
    COMPILER_VERSION = '4.8.4'
    GIT_VERSION = 'v1.1.0-rc0-61-g1ec6ed5'
    GRAPH_DEF_VERSION = 21
    GRAPH_DEF_VERSION_MIN_CONSUMER = 0
    GRAPH_DEF_VERSION_MIN_PRODUCER = 0
    QUANTIZED_DTYPES = frozenset({tf.qint8, tf.quint8, tf.qint32, tf.qint1...
    VERSION = '1.1.0'
    __compiler_version__ = '4.8.4'
    __git_version__ = 'v1.1.0-rc0-61-g1ec6ed5'
    bfloat16 = tf.bfloat16
    bool = tf.bool
    complex128 = tf.complex128
    complex64 = tf.complex64
    contrib = <tensorflow._LazyContribLoader object>
    double = tf.float64
    float16 = tf.float16
    float32 = tf.float32
    float64 = tf.float64
    half = tf.float16
    int16 = tf.int16
    int32 = tf.int32
    int64 = tf.int64
    int8 = tf.int8
    newaxis = None
    qint16 = tf.qint16
    qint32 = tf.qint32
    qint8 = tf.qint8
    quint16 = tf.quint16
    quint8 = tf.quint8
    resource = tf.resource
    string = tf.string
    uint16 = tf.uint16
    uint8 = tf.uint8

VERSION
    1.1.0

FILE
    /home/bog/anaconda3/lib/python3.6/site-packages/tensorflow/__init__.py

tab有自动补全功能，比如tf. 后面按一个tab键，他会把后面可以接上的函数列出来

import sklearn

dir(sklearn.clone)

['__annotations__',
 '__call__',
 '__class__',
 '__closure__',
 '__code__',
 '__defaults__',
 '__delattr__',
 '__dict__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__get__',
 '__getattribute__',
 '__globals__',
 '__gt__',
 '__hash__',
 '__init__',
 '__init_subclass__',
 '__kwdefaults__',
 '__le__',
 '__lt__',
 '__module__',
 '__name__',
 '__ne__',
 '__new__',
 '__qualname__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__setattr__',
 '__sizeof__',
 '__str__',
 '__subclasshook__']

dir(tf)

['AggregationMethod',
 'Assert',
 'AttrValue',
 'COMPILER_VERSION',
 'ConditionalAccumulator',
 'ConditionalAccumulatorBase',
 'ConfigProto',
 'DType',
 'DeviceSpec',
 'Dimension',
 'Event',
 'FIFOQueue',
 'FixedLenFeature',
 'FixedLenSequenceFeature',
 'FixedLengthRecordReader',
 'GIT_VERSION',
 'GPUOptions',
 'GRAPH_DEF_VERSION',
 'GRAPH_DEF_VERSION_MIN_CONSUMER',
 'GRAPH_DEF_VERSION_MIN_PRODUCER',
 'Graph',
 'GraphDef',
 'GraphKeys',
 'GraphOptions',
 'HistogramProto',
 'IdentityReader',
 'IndexedSlices',
 'InteractiveSession',
 'LogMessage',
 'NameAttrList',
 'NoGradient',
 'NodeDef',
 'NotDifferentiable',
 'OpError',
 'Operation',
 'OptimizerOptions',
 'PaddingFIFOQueue',
 'Print',
 'PriorityQueue',
 'QUANTIZED_DTYPES',
 'QueueBase',
 'RandomShuffleQueue',
 'ReaderBase',
 'RegisterGradient',
 'RunMetadata',
 'RunOptions',
 'Session',
 'SessionLog',
 'SparseConditionalAccumulator',
 'SparseFeature',
 'SparseTensor',
 'SparseTensorValue',
 'Summary',
 'TFRecordReader',
 'Tensor',
 'TensorArray',
 'TensorInfo',
 'TensorShape',
 'TextLineReader',
 'VERSION',
 'VarLenFeature',
 'Variable',
 'VariableScope',
 'WholeFileReader',
 '_LazyContribLoader',
 '__builtins__',
 '__cached__',
 '__compiler_version__',
 '__doc__',
 '__file__',
 '__git_version__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 '__version__',
 'abs',
 'accumulate_n',
 'acos',
 'add',
 'add_check_numerics_ops',
 'add_n',
 'add_to_collection',
 'all_variables',
 'app',
 'arg_max',
 'arg_min',
 'argmax',
 'argmin',
 'as_dtype',
 'as_string',
 'asin',
 'assert_equal',
 'assert_greater',
 'assert_greater_equal',
 'assert_integer',
 'assert_less',
 'assert_less_equal',
 'assert_negative',
 'assert_non_negative',
 'assert_non_positive',
 'assert_none_equal',
 'assert_positive',
 'assert_proper_iterable',
 'assert_rank',
 'assert_rank_at_least',
 'assert_type',
 'assert_variables_initialized',
 'assign',
 'assign_add',
 'assign_sub',
 'atan',
 'batch_to_space',
 'batch_to_space_nd',
 'betainc',
 'bfloat16',
 'bincount',
 'bitcast',
 'bool',
 'boolean_mask',
 'broadcast_dynamic_shape',
 'broadcast_static_shape',
 'case',
 'cast',
 'ceil',
 'check_numerics',
 'cholesky',
 'cholesky_solve',
 'clip_by_average_norm',
 'clip_by_global_norm',
 'clip_by_norm',
 'clip_by_value',
 'compat',
 'complex',
 'complex128',
 'complex64',
 'concat',
 'cond',
 'confusion_matrix',
 'conj',
 'constant',
 'constant_initializer',
 'container',
 'contrib',
 'control_dependencies',
 'convert_to_tensor',
 'convert_to_tensor_or_indexed_slices',
 'convert_to_tensor_or_sparse_tensor',
 'cos',
 'count_nonzero',
 'count_up_to',
 'create_partitioned_variables',
 'cross',
 'cumprod',
 'cumsum',
 'decode_base64',
 'decode_csv',
 'decode_json_example',
 'decode_raw',
 'delete_session_tensor',
 'depth_to_space',
 'dequantize',
 'deserialize_many_sparse',
 'device',
 'diag',
 'diag_part',
 'digamma',
 'div',
 'divide',
 'double',
 'dynamic_partition',
 'dynamic_stitch',
 'edit_distance',
 'einsum',
 'encode_base64',
 'equal',
 'erf',
 'erfc',
 'errors',
 'estimator',
 'exp',
 'expand_dims',
 'expm1',
 'extract_image_patches',
 'eye',
 'fake_quant_with_min_max_args',
 'fake_quant_with_min_max_args_gradient',
 'fake_quant_with_min_max_vars',
 'fake_quant_with_min_max_vars_gradient',
 'fake_quant_with_min_max_vars_per_channel',
 'fake_quant_with_min_max_vars_per_channel_gradient',
 'fft',
 'fft2d',
 'fft3d',
 'fill',
 'fixed_size_partitioner',
 'flags',
 'float16',
 'float32',
 'float64',
 'floor',
 'floor_div',
 'floordiv',
 'floormod',
 'foldl',
 'foldr',
 'gather',
 'gather_nd',
 'get_collection',
 'get_collection_ref',
 'get_default_graph',
 'get_default_session',
 'get_local_variable',
 'get_seed',
 'get_session_handle',
 'get_session_tensor',
 'get_variable',
 'get_variable_scope',
 'gfile',
 'global_norm',
 'global_variables',
 'global_variables_initializer',
 'gradients',
 'graph_util',
 'greater',
 'greater_equal',
 'group',
 'half',
 'hessians',
 'histogram_fixed_width',
 'identity',
 'ifft',
 'ifft2d',
 'ifft3d',
 'igamma',
 'igammac',
 'imag',
 'image',
 'import_graph_def',
 'initialize_all_tables',
 'initialize_all_variables',
 'initialize_local_variables',
 'initialize_variables',
 'int16',
 'int32',
 'int64',
 'int8',
 'invert_permutation',
 'is_finite',
 'is_inf',
 'is_nan',
 'is_non_decreasing',
 'is_numeric_tensor',
 'is_strictly_increasing',
 'is_variable_initialized',
 'layers',
 'lbeta',
 'less',
 'less_equal',
 'lgamma',
 'lin_space',
 'linspace',
 'load_file_system_library',
 'load_op_library',
 'local_variables',
 'local_variables_initializer',
 'log',
 'log1p',
 'logging',
 'logical_and',
 'logical_not',
 'logical_or',
 'logical_xor',
 'losses',
 'make_template',
 'map_fn',
 'matching_files',
 'matmul',
 'matrix_band_part',
 'matrix_determinant',
 'matrix_diag',
 'matrix_diag_part',
 'matrix_inverse',
 'matrix_set_diag',
 'matrix_solve',
 'matrix_solve_ls',
 'matrix_transpose',
 'matrix_triangular_solve',
 'maximum',
 'meshgrid',
 'metrics',
 'min_max_variable_partitioner',
 'minimum',
 'mod',
 'model_variables',
 'moving_average_variables',
 'multinomial',
 'multiply',
 'name_scope',
 'negative',
 'newaxis',
 'nn',
 'no_op',
 'no_regularizer',
 'norm',
 'not_equal',
 'one_hot',
 'ones',
 'ones_initializer',
 'ones_like',
 'op_scope',
 'orthogonal_initializer',
 'pad',
 'parallel_stack',
 'parse_example',
 'parse_single_example',
 'parse_single_sequence_example',
 'parse_tensor',
 'placeholder',
 'placeholder_with_default',
 'polygamma',
 'pow',
 'py_func',
 'python_io',
 'pywrap_tensorflow',
 'qint16',
 'qint32',
 'qint8',
 'qr',
 'quantize_v2',
 'quantized_concat',
 'quint16',
 'quint8',
 'random_crop',
 'random_gamma',
 'random_normal',
 'random_normal_initializer',
 'random_poisson',
 'random_shuffle',
 'random_uniform',
 'random_uniform_initializer',
 'range',
 'rank',
 'read_file',
 'real',
 'realdiv',
 'reciprocal',
 'reduce_all',
 'reduce_any',
 'reduce_join',
 'reduce_logsumexp',
 'reduce_max',
 'reduce_mean',
 'reduce_min',
 'reduce_prod',
 'reduce_sum',
 'register_tensor_conversion_function',
 'report_uninitialized_variables',
 'required_space_to_batch_paddings',
 'reset_default_graph',
 'reshape',
 'resource',
 'resource_loader',
 'reverse',
 'reverse_sequence',
 'reverse_v2',
 'rint',
 'round',
 'rsqrt',
 'saturate_cast',
 'saved_model',
 'scalar_mul',
 'scan',
 'scatter_add',
 'scatter_div',
 'scatter_mul',
 'scatter_nd',
 'scatter_nd_add',
 'scatter_nd_sub',
 'scatter_nd_update',
 'scatter_sub',
 'scatter_update',
 'sdca',
 'segment_max',
 'segment_mean',
 'segment_min',
 'segment_prod',
 'segment_sum',
 'self_adjoint_eig',
 'self_adjoint_eigvals',
 'sequence_mask',
 'serialize_many_sparse',
 'serialize_sparse',
 'set_random_seed',
 'setdiff1d',
 'sets',
 'shape',
 'shape_n',
 'sigmoid',
 'sign',
 'sin',
 'size',
 'slice',
 'space_to_batch',
 'space_to_batch_nd',
 'space_to_depth',
 'sparse_add',
 'sparse_concat',
 'sparse_fill_empty_rows',
 'sparse_mask',
 'sparse_matmul',
 'sparse_maximum',
 'sparse_merge',
 'sparse_minimum',
 'sparse_placeholder',
 'sparse_reduce_sum',
 'sparse_reduce_sum_sparse',
 'sparse_reorder',
 'sparse_reset_shape',
 'sparse_reshape',
 'sparse_retain',
 'sparse_segment_mean',
 'sparse_segment_sqrt_n',
 'sparse_segment_sum',
 'sparse_softmax',
 'sparse_split',
 'sparse_tensor_dense_matmul',
 'sparse_tensor_to_dense',
 'sparse_to_dense',
 'sparse_to_indicator',
 'sparse_transpose',
 'spectral',
 'split',
 'sqrt',
 'square',
 'squared_difference',
 'squeeze',
 'stack',
 'stop_gradient',
 'strided_slice',
 'string',
 'string_join',
 'string_split',
 'string_to_hash_bucket',
 'string_to_hash_bucket_fast',
 'string_to_hash_bucket_strong',
 'string_to_number',
 'substr',
 'subtract',
 'summary',
 'svd',
 'sysconfig',
 'tables_initializer',
 'tan',
 'tanh',
 'tensordot',
 'test',
 'tile',
 'to_bfloat16',
 'to_double',
 'to_float',
 'to_int32',
 'to_int64',
 'trace',
 'train',
 'trainable_variables',
 'transpose',
 'truediv',
 'truncated_normal',
 'truncated_normal_initializer',
 'truncatediv',
 'truncatemod',
 'tuple',
 'uint16',
 'uint8',
 'uniform_unit_scaling_initializer',
 'unique',
 'unique_with_counts',
 'unsorted_segment_max',
 'unsorted_segment_sum',
 'unstack',
 'user_ops',
 'variable_axis_size_partitioner',
 'variable_op_scope',
 'variable_scope',
 'variables_initializer',
 'verify_tensor_all_finite',
 'where',
 'while_loop',
 'write_file',
 'zeros',
 'zeros_initializer',
 'zeros_like',
 'zeta']

python的运算

+/-/*//

6/4

1.5

6//4

4**0.5

2.0

4%3

python基本数据类型，变量，运算，表达式

3.变量

基本数据类型：

int 整数
float 浮点数
str 字符窜
bool型
type（）

X = 12

type(X)

int

y = -5.32
type(y)

float

z = 'bog5d'
type(z)

str

z[:3]

'bog'

a ="bog\n5d"
print(a)

bog
5d

a1 = 'bog\n5d'
print(a1)

bog
5d

d = True
type(d)

bool

e = pd.DataFrame()
type(e)

pandas.core.frame.DataFrame

表达式

Python 会用表达式

### 切片
a = 'bog5diloveyou'
a

'bog5diloveyou'

a[4]

'd'

a[-3]

'y'

a[1:4]
#第二位到第五位
#左闭右开，是切片原则

'og5'

a[-6:-2]

'ovey'

a[2:]

'g5diloveyou'

a[:5]

'bog5d'

字符窜函数

dir(str)

['__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',
 'isdecimal',
 'isdigit',
 'isidentifier',
 'islower',
 'isnumeric',
 'isprintable',
 'isspace',
 'istitle',
 'isupper',
 'join',
 'ljust',
 'lower',
 'lstrip',
 'maketrans',
 'partition',
 'replace',
 'rfind',
 'rindex',
 'rjust',
 'rpartition',
 'rsplit',
 'rstrip',
 'split',
 'splitlines',
 'startswith',
 'strip',
 'swapcase',
 'title',
 'translate',
 'upper',
 'zfill']

a.upper()

'BOG5DILOVEYOU'

a.endswith('u')

True

b= "      "+a+"-yes!!!"
b

'      bog5diloveyou-yes!!!'

b=b.strip()

c = "我 爱 达州"

c.split(" ")

['我', '爱', '达州']

b.find('5d')

list列表

list是Python中的数据结构，表示一连串的数据的集合（有序的）

names = ["bobi","eva","joe","二狗","李雷","hanmeimei"]
#元素是任何玩意儿，都可以

type(list)

type

type(names)

list

dir(list)

['__add__',
 '__class__',
 '__contains__',
 '__delattr__',
 '__delitem__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__getitem__',
 '__gt__',
 '__hash__',
 '__iadd__',
 '__imul__',
 '__init__',
 '__init_subclass__',
 '__iter__',
 '__le__',
 '__len__',
 '__lt__',
 '__mul__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__reversed__',
 '__rmul__',
 '__setattr__',
 '__setitem__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 'append',
 'clear',
 'copy',
 'count',
 'extend',
 'index',
 'insert',
 'pop',
 'remove',
 'reverse',
 'sort']

len(names)

mixed = ['bobi',2,3.14,[1,2,34]]
len(mixed)

### 切片
mixed[1]

mixed[3]

[1, 2, 34]

mixed[3][2]

mixed[2:]

[3.14, [1, 2, 34]]

split<==>join(对一堆字符串组成的list去做的）

names

['bobi', 'eva', 'joe', '二狗', '李雷', 'hanmeimei']

mixed

['bobi', 2, 3.14, [1, 2, 34]]

" ## ".join(names)

'bobi ## eva ## joe ## 二狗 ## 李雷 ## hanmeimei'

"+".join(names)

'bobi+eva+joe+二狗+李雷+hanmeimei'

append(往尾巴上去追加)

mixed

['bobi', 2, 3.14, [1, 2, 34], '36', '36', '36']

mixed.extend([54,66,99])

mixed

['bobi', 2, 3.14, [1, 2, 34], '36', '36', '36', 54, 66, 99]

mixed.index('36')

mixed.pop()

mixed

['bobi', 2, 3.14, [1, 2, 34], '36', '36', '36', 54, 66]

流程控制

条件判断 if else

#判断一个老人
age = 25
if age>60:
    print("一把老骨头啊")
elif age>35:
    print("形状之年啊")
else:
    print("小年轻")

小年轻

for name in names:
    print(name)

bobi
eva
joe
二狗
李雷
hanmeimei

for i,name in enumerate(names):
    print(i,name)

0 bobi
1 eva
2 joe
3 二狗
4 李雷
5 hanmeimei

i = 0
while i<10:
    print(i)
    i+=1

i = 0
while True:
    print(i)
    i+=1
    if i>6:
        break

i = 0
while True:
    i+=1
    if i%3== 0:
        continue
    print(i)
    if i>6:
        break

for name in names:
    print('my name is'+name)

my name isbobi
my name iseva
my name isjoe
my name is二狗
my name is李雷
my name ishanmeimei

###列表推到式
['my name is '+name for name in names]

['my name is bobi',
 'my name is eva',
 'my name is joe',
 'my name is 二狗',
 'my name is 李雷',
 'my name is hanmeimei']

num_list = list(range(1,16))

[num**2 for num in num_list]

[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225]

[num**2 for num in num_list if num%2==1]

[1, 9, 25, 49, 81, 121, 169, 225]

[num**2 for num in num_list if (num%2==1 and num<5)]

[1, 9]

set/ 集合

num_list = num_list*2
num_list

[1,
 2,
 3,
 4,
 5,
 6,
 7,
 8,
 9,
 10,
 11,
 12,
 13,
 14,
 15,
 1,
 2,
 3,
 4,
 5,
 6,
 7,
 8,
 9,
 10,
 11,
 12,
 13,
 14,
 15]

set(num_list)

{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}

字典/dictionary

有时候，我们需要一下key—value这样的数据。键值对

legs ={'spider:8','pig:4','duck:2'}

legs.{'pig'}

  File "<ipython-input-80-9796474270bf>", line 1
    legs.{'pig'}
         ^
SyntaxError: invalid syntax

list(legs.key())

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-81-e76d2b520d0a> in <module>()
----> 1 list(legs.key())


AttributeError: 'set' object has no attribute 'key'

字典推到式

my_list = list(range(10))
my_list

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

{num:num**3 for num in my_list}

{0: 0, 1: 1, 2: 8, 3: 27, 4: 64, 5: 125, 6: 216, 7: 343, 8: 512, 9: 729}

###高级排序
my_list = [5,1,4,2,3]
my_list.sort()#成员函数

my_list

[1, 2, 3, 4, 5]

tmp_list=[5,4,3,1,2]
sorted(tmp_list)#外部函数，不会改变数据序列本身

[1, 2, 3, 4, 5]

tmp_list

[5, 4, 3, 1, 2]

sorted(tmp_list,reverse=False)

[1, 2, 3, 4, 5]

strs =['ccc','aaaa','eeeeeee','kdjehd']

sorted(strs)

['aaaa', 'ccc', 'eeeeeee', 'kdjehd']

sorted(strs,key=len)#根据key对元素做处理后的结果排序

['ccc', 'aaaa', 'kdjehd', 'eeeeeee']

tmp_strs= ['aa','CC','BB','ZZ']

sorted(tmp_strs,key=str.lower)

['aa', 'BB', 'CC', 'ZZ']

函数

def 关键字
括号包含参数列表
语句需要缩进

def my_sum(a,b):
    return (a+b+1)**(1/3)

my_sum(3,4)

2.0

def fib(n):
    a,b = 0,1
    for i in range(n):
        print(a,end=" ")
        a,b=b,a+b
    print()

fib(15)

0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

def my_mul(x,y=3):
    return x*y

my_mul(4,5)

my_mul(4)

文件的读写

!ls

2017-11-25 19-16-05屏幕截图.png  波哥linux创建的  文档 稀牛学院课1.ipynb

!2017-11-25\ 19-16-05屏幕截图.png

/bin/sh: 1: 2017-11-25 19-16-05屏幕截图.png: not found

!pwd

/home/bog/图片

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.

f = open('./无标题文档','r')

f.read()

"sorted(tmp_list)\ndsjfsjofdsfjsdajfdsa\nfjdsiojfds\n4\nkds'\n"

f.close()

f = open('./无标题文档','r')
f.readlines()

['sorted(tmp_list)\n',
 'dsjfsjofdsfjsdajfdsa\n',
 'fjdsiojfds\n',
 '4\n',
 "kds'\n"]

f.close()

for line in open('./无标题文档','r'):
    print(len(line.strip()))

作业-统计词频

给你一个文件，统计这个文档中出现了的单词，出现的次数。

正则表达式

tem_str = "有谁喜欢郭金明的书，那么韩寒呢"

tem_str.find("郭金明")

匹配的是一个模式，我让你找到所有邮箱字符串

email_str = "my email is wangbo8805@gmail.com,his email is test@test.net,goodbye"

import re #正则表达式
#\d 数字
# \2 字母，数字，下划线。。。。
#\s 所有的空格 空白 或者tab 换行
# + 贪婪匹配
# * 匹配0次或者若干次
match=re.search(r'[\w.-]+@[\w.-]+',email_str)
if match:
    print(match.group())

wangbo8805@gmail.com

hanxiaoyang.ml@gmail.com寒晓阳的邮箱

emails = re.findall(r'[\w.-]+@[\w.-]+',email_str)
if emails:
    for email in emails:
        print(email)

wangbo8805@gmail.com
test@test.net