
import numpy as np
import pandas as pd


np.__version__

'1.23.5'


np.array([1,1.0,'2']) # 强制转换成同一种类型

array(['1', '1.0', '2'], dtype='<U32')


np.array([1,1.0,'2'],dtype=int)

array([1, 1, 2])


np.ones((3,5))

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


np.full((3,5),1.231)

array([[1.231, 1.231, 1.231, 1.231, 1.231],
       [1.231, 1.231, 1.231, 1.231, 1.231],
       [1.231, 1.231, 1.231, 1.231, 1.231]])


np.arange(0, 20)

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19])


np.random.normal(0, 1, (3,3))

array([[ 0.11949003,  0.51600352, -0.60939972],
       [-0.44918345,  0.80383762, -0.20336444],
       [ 0.20335392, -1.23692957, -1.40778524]])


np.eye(3)

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


np.tile?

Signature: np.tile(A, reps)
Docstring:
Construct an array by repeating A the number of times given by reps.

If `reps` has length ``d``, the result will have dimension of
``max(d, A.ndim)``.

If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
or shape (1, 1, 3) for 3-D replication. If this is not the desired
behavior, promote `A` to d-dimensions manually before calling this
function.

If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
(1, 1, 2, 2).

Note : Although tile may be used for broadcasting, it is strongly
recommended to use numpy's broadcasting operations and functions.

Parameters
----------
A : array_like
    The input array.
reps : array_like
    The number of repetitions of `A` along each axis.

Returns
-------
c : ndarray
    The tiled output array.

See Also
--------
repeat : Repeat elements of an array.
broadcast_to : Broadcast an array to a new shape

Examples
--------
>>> a = np.array([0, 1, 2])
>>> np.tile(a, 2)
array([0, 1, 2, 0, 1, 2])
>>> np.tile(a, (2, 2))
array([[0, 1, 2, 0, 1, 2],
       [0, 1, 2, 0, 1, 2]])
>>> np.tile(a, (2, 1, 2))
array([[[0, 1, 2, 0, 1, 2]],
       [[0, 1, 2, 0, 1, 2]]])

>>> b = np.array([[1, 2], [3, 4]])
>>> np.tile(b, 2)
array([[1, 2, 1, 2],
       [3, 4, 3, 4]])
>>> np.tile(b, (2, 1))
array([[1, 2],
       [3, 4],
       [1, 2],
       [3, 4]])

>>> c = np.array([1,2,3,4])
>>> np.tile(c,(4,1))
array([[1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4]])
File:      ~/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/shape_base.py
Type:      function


np.tile(10,3)

array([10, 10, 10])


np.tile([1,2,3],3)

array([1, 2, 3, 1, 2, 3, 1, 2, 3])


np.repeat?

Signature: np.repeat(a, repeats, axis=None)
Docstring:
Repeat elements of an array.

Parameters
----------
a : array_like
    Input array.
repeats : int or array of ints
    The number of repetitions for each element.  `repeats` is broadcasted
    to fit the shape of the given axis.
axis : int, optional
    The axis along which to repeat values.  By default, use the
    flattened input array, and return a flat output array.

Returns
-------
repeated_array : ndarray
    Output array which has the same shape as `a`, except along
    the given axis.

See Also
--------
tile : Tile an array.
unique : Find the unique elements of an array.

Examples
--------
>>> np.repeat(3, 4)
array([3, 3, 3, 3])
>>> x = np.array([[1,2],[3,4]])
>>> np.repeat(x, 2)
array([1, 1, 2, 2, 3, 3, 4, 4])
>>> np.repeat(x, 3, axis=1)
array([[1, 1, 1, 2, 2, 2],
       [3, 3, 3, 4, 4, 4]])
>>> np.repeat(x, [1, 2], axis=0)
array([[1, 2],
       [3, 4],
       [3, 4]])
File:      ~/opt/anaconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py
Type:      function


np.asarray([1,2,3]).repeat(3)

array([1, 1, 1, 2, 2, 2, 3, 3, 3])


a = np.random.normal(0, 1, (3,3))
a

array([[-1.26401894,  0.13314849, -0.81723518],
       [-0.35654833,  1.03394826,  0.05554441],
       [-0.22990499,  0.14560186, -0.26441533]])


a[0,1]

0.1331484866845464


a[0:2]

array([[-1.26401894,  0.13314849, -0.81723518],
       [-0.35654833,  1.03394826,  0.05554441]])


a[0:2,1]

array([0.13314849, 1.03394826])


a[0,1] = 1000

a

array([[-1.26401894e+00,  1.00000000e+03, -8.17235181e-01],
       [-3.56548330e-01,  1.03394826e+00,  5.55444060e-02],
       [-2.29904987e-01,  1.45601862e-01, -2.64415325e-01]])


a[0:3:2]

array([[-1.26401894e+00,  1.00000000e+03, -8.17235181e-01],
       [-2.29904987e-01,  1.45601862e-01, -2.64415325e-01]])


x = np.array([1, 2, 3])
y = np.array([3, 2, 1])
z = [21,21,21]
np.concatenate([x,y,z])

array([ 1,  2,  3,  3,  2,  1, 21, 21, 21])


a1 = np.ones((3,3))
a2 = np.random.normal(0, 1, (3,3))

a1

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

a2

array([[-0.48831842, -0.30504266,  0.82149508],
       [-1.71501757, -0.41102703, -0.38721744],
       [ 1.20687861,  0.0802707 , -1.83544937]])


np.concatenate([a1,a2])

array([[ 1.        ,  1.        ,  1.        ],
       [ 1.        ,  1.        ,  1.        ],
       [ 1.        ,  1.        ,  1.        ],
       [-0.48831842, -0.30504266,  0.82149508],
       [-1.71501757, -0.41102703, -0.38721744],
       [ 1.20687861,  0.0802707 , -1.83544937]])


np.concatenate([a1,a2], axis=1)

array([[ 1.        ,  1.        ,  1.        , -0.48831842, -0.30504266,
         0.82149508],
       [ 1.        ,  1.        ,  1.        , -1.71501757, -0.41102703,
        -0.38721744],
       [ 1.        ,  1.        ,  1.        ,  1.20687861,  0.0802707 ,
        -1.83544937]])


a1 = np.array(range(10))
a1[0:3]

array([0, 1, 2])

a1

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


a1.shape

(10,)


a1 = a1.reshape((10,1))


a1.shape

(10, 1)

a1

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


a1 = a1.reshape((1,10))


a1.shape

(1, 10)

a1

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


a1.reshape((2,6))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[39], line 1
----> 1 a1.reshape((2,6))

ValueError: cannot reshape array of size 10 into shape (2,6)


a1 = a1.reshape((2,5))

a1

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


a2 = np.array(range(10,20)).reshape((2,5))

a2

array([[10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19]])

a1

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


np.vstack([a1,a2])

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19]])


np.hstack([a1,a2])

array([[ 0,  1,  2,  3,  4, 10, 11, 12, 13, 14],
       [ 5,  6,  7,  8,  9, 15, 16, 17, 18, 19]])


a1 + 1000

array([[1000, 1001, 1002, 1003, 1004],
       [1005, 1006, 1007, 1008, 1009]])


a1 * a2

array([[  0,  11,  24,  39,  56],
       [ 75,  96, 119, 144, 171]])


list1 = [1,2,3] # some manipulations is not possible with raw python
list2 = [4,5,6]


list1 * list2

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[50], line 1
----> 1 list1 * list2

TypeError: can't multiply sequence by non-int of type 'list'


list1 + list2

[1, 2, 3, 4, 5, 6]


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

array([1, 2, 3, 4, 5, 6])


x.ravel(order='F')

array([1, 4, 2, 5, 3, 6])

a1

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


a1.max()

9


a1.max(axis=1)

array([4, 9])


a1.max(axis=0)

array([5, 6, 7, 8, 9])


a1.sum()

45


a1.sum(axis=0)

array([ 5,  7,  9, 11, 13])


a1.sum(axis=1)

array([10, 35])


a1.mean()

4.5

a1

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


a1.mean(axis=1)

array([2., 7.])


a1.mean(axis=0)

array([2.5, 3.5, 4.5, 5.5, 6.5])


import numpy.ma as ma


x = np.array([1,2,3,-1,4])


mx = ma.masked_array(x, mask=[0,0,0,1,0])

mx

masked_array(data=[1, 2, 3, --, 4],
             mask=[False, False, False,  True, False],
       fill_value=999999)


mx.mean()

2.5


# -999 as an invalid value, as sometimes seen in some databases
a = np.array([0,1,2,-999])
ma.masked_values(a, -999)

masked_array(data=[0, 1, 2, --],
             mask=[False, False, False,  True],
       fill_value=-999)


df = pd.DataFrame({'name':[chr(i) for i in range(ord('a'),ord('z')+1)],
                   'value':range(26)}) # string.ascii_lowercase provides the alphabet too.
df


data = [['tom', 10], ['nick', 15], ['juli', 14]]
df = pd.DataFrame(data, columns = ['Name', 'Age'], index=['a','b','c'])
df


import pandas._testing as tm


dir(tm)

['ALL_INT_EA_DTYPES',
 'ALL_INT_NUMPY_DTYPES',
 'ALL_NUMPY_DTYPES',
 'ALL_REAL_NUMPY_DTYPES',
 'BOOL_DTYPES',
 'BYTES_DTYPES',
 'BaseMaskedArray',
 'COMPLEX_DTYPES',
 'Callable',
 'Categorical',
 'CategoricalIndex',
 'ContextManager',
 'Counter',
 'DATETIME64_DTYPES',
 'DataFrame',
 'DatetimeIndex',
 'Decimal',
 'Dtype',
 'EMPTY_STRING_PATTERN',
 'ENDIAN',
 'ExtensionArray',
 'FLOAT_EA_DTYPES',
 'FLOAT_NUMPY_DTYPES',
 'Float64Index',
 'Index',
 'Int64Index',
 'IntervalIndex',
 'Iterable',
 'MultiIndex',
 'NARROW_NP_DTYPES',
 'NDArrayBackedExtensionArray',
 'NP_NAT_OBJECTS',
 'NULL_OBJECTS',
 'NumericIndex',
 'OBJECT_DTYPES',
 'PandasArray',
 'RNGContext',
 'RangeIndex',
 'SIGNED_INT_EA_DTYPES',
 'SIGNED_INT_NUMPY_DTYPES',
 'STRING_DTYPES',
 'Series',
 'SubclassedCategorical',
 'SubclassedDataFrame',
 'SubclassedSeries',
 'TIMEDELTA64_DTYPES',
 'TYPE_CHECKING',
 'UInt64Index',
 'UNSIGNED_INT_EA_DTYPES',
 'UNSIGNED_INT_NUMPY_DTYPES',
 '_K',
 '_N',
 '__all__',
 '__annotations__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 '_create_missing_idx',
 '_io',
 '_make_skipna_wrapper',
 '_random',
 '_testing_mode_warnings',
 '_warnings',
 'all_timeseries_index_generator',
 'annotations',
 'assert_almost_equal',
 'assert_attr_equal',
 'assert_categorical_equal',
 'assert_class_equal',
 'assert_contains_all',
 'assert_copy',
 'assert_datetime_array_equal',
 'assert_dict_equal',
 'assert_equal',
 'assert_extension_array_equal',
 'assert_frame_equal',
 'assert_index_equal',
 'assert_indexing_slices_equivalent',
 'assert_interval_array_equal',
 'assert_is_sorted',
 'assert_is_valid_plot_return_object',
 'assert_metadata_equivalent',
 'assert_numpy_array_equal',
 'assert_period_array_equal',
 'assert_produces_warning',
 'assert_series_equal',
 'assert_sp_array_equal',
 'assert_timedelta_array_equal',
 'asserters',
 'at',
 'bdate_range',
 'box_expected',
 'byteorder',
 'can_set_locale',
 'close',
 'collections',
 'compat',
 'contexts',
 'convert_rows_list_to_csv_str',
 'cython_table',
 'datetime',
 'decompress_file',
 'ensure_clean',
 'ensure_clean_dir',
 'ensure_safe_environment_variables',
 'equalContents',
 'external_error_raised',
 'extract_array',
 'getCols',
 'getMixedTypeDict',
 'getPeriodData',
 'getSeriesData',
 'getTimeSeriesData',
 'get_cython_table_params',
 'get_dtype',
 'get_locales',
 'get_obj',
 'get_op_from_name',
 'getitem',
 'iat',
 'iloc',
 'index_subclass_makers_generator',
 'is_float_dtype',
 'is_integer_dtype',
 'is_sequence',
 'is_unsigned_integer_dtype',
 'loc',
 'makeBoolIndex',
 'makeCategoricalIndex',
 'makeCustomDataframe',
 'makeCustomIndex',
 'makeDataFrame',
 'makeDateIndex',
 'makeFloatIndex',
 'makeFloatSeries',
 'makeIntIndex',
 'makeIntervalIndex',
 'makeMissingDataframe',
 'makeMixedDataFrame',
 'makeMultiIndex',
 'makeNumericIndex',
 'makeObjectSeries',
 'makePeriodFrame',
 'makePeriodIndex',
 'makePeriodSeries',
 'makeRangeIndex',
 'makeStringIndex',
 'makeStringSeries',
 'makeTimeDataFrame',
 'makeTimeSeries',
 'makeTimedeltaIndex',
 'makeUIntIndex',
 'make_rand_series',
 'maybe_produces_warning',
 'network',
 'np',
 'operator',
 'os',
 'pa_version_under1p01',
 'pandas_dtype',
 'pd',
 'raise_assert_detail',
 'randbool',
 'rands',
 'rands_array',
 're',
 'reset_display_options',
 'reset_testing_mode',
 'round_trip_localpath',
 'round_trip_pathlib',
 'round_trip_pickle',
 'set_locale',
 'set_testing_mode',
 'set_timezone',
 'setitem',
 'shares_memory',
 'string',
 'to_array',
 'use_numexpr',
 'warnings',
 'with_csv_dialect',
 'write_to_compressed']


tm.makeDataFrame()


tm.makeMixedDataFrame()


df1 = tm.makeTimeDataFrame()
df1


df2 = tm.makePeriodFrame()
df2


df1.index

DatetimeIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
               '2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',
               '2000-01-13', '2000-01-14', '2000-01-17', '2000-01-18',
               '2000-01-19', '2000-01-20', '2000-01-21', '2000-01-24',
               '2000-01-25', '2000-01-26', '2000-01-27', '2000-01-28',
               '2000-01-31', '2000-02-01', '2000-02-02', '2000-02-03',
               '2000-02-04', '2000-02-07', '2000-02-08', '2000-02-09',
               '2000-02-10', '2000-02-11'],
              dtype='datetime64[ns]', freq='B')


df1.index.day_of_week

Int64Index([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1,
            2, 3, 4, 0, 1, 2, 3, 4],
           dtype='int64')


df1.index.shift(periods=1) # 时间序列index的shift，会按照指定的period或freq平移。注意：如果没有时间序列，则直接平移，见df1['A']例

DatetimeIndex(['2000-01-04', '2000-01-05', '2000-01-06', '2000-01-07',
               '2000-01-10', '2000-01-11', '2000-01-12', '2000-01-13',
               '2000-01-14', '2000-01-17', '2000-01-18', '2000-01-19',
               '2000-01-20', '2000-01-21', '2000-01-24', '2000-01-25',
               '2000-01-26', '2000-01-27', '2000-01-28', '2000-01-31',
               '2000-02-01', '2000-02-02', '2000-02-03', '2000-02-04',
               '2000-02-07', '2000-02-08', '2000-02-09', '2000-02-10',
               '2000-02-11', '2000-02-14'],
              dtype='datetime64[ns]', freq='B')


df1.index.shift(freq='D')

DatetimeIndex(['2000-01-04', '2000-01-05', '2000-01-06', '2000-01-07',
               '2000-01-08', '2000-01-11', '2000-01-12', '2000-01-13',
               '2000-01-14', '2000-01-15', '2000-01-18', '2000-01-19',
               '2000-01-20', '2000-01-21', '2000-01-22', '2000-01-25',
               '2000-01-26', '2000-01-27', '2000-01-28', '2000-01-29',
               '2000-02-01', '2000-02-02', '2000-02-03', '2000-02-04',
               '2000-02-05', '2000-02-08', '2000-02-09', '2000-02-10',
               '2000-02-11', '2000-02-12'],
              dtype='datetime64[ns]', freq=None)


df1.index.shift(freq='2D')

DatetimeIndex(['2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
               '2000-01-09', '2000-01-12', '2000-01-13', '2000-01-14',
               '2000-01-15', '2000-01-16', '2000-01-19', '2000-01-20',
               '2000-01-21', '2000-01-22', '2000-01-23', '2000-01-26',
               '2000-01-27', '2000-01-28', '2000-01-29', '2000-01-30',
               '2000-02-02', '2000-02-03', '2000-02-04', '2000-02-05',
               '2000-02-06', '2000-02-09', '2000-02-10', '2000-02-11',
               '2000-02-12', '2000-02-13'],
              dtype='datetime64[ns]', freq=None)

df1


df1['A']

2000-01-03   -0.256639
2000-01-04   -0.441481
2000-01-05    0.687317
2000-01-06    0.524671
2000-01-07    1.436570
2000-01-10    0.047872
2000-01-11    0.930656
2000-01-12    0.907508
2000-01-13    0.539813
2000-01-14    0.157760
2000-01-17    0.309461
2000-01-18    0.010488
2000-01-19    1.299948
2000-01-20   -1.287793
2000-01-21   -0.100412
2000-01-24    0.404562
2000-01-25    0.126925
2000-01-26   -0.960476
2000-01-27    2.786106
2000-01-28    1.691881
2000-01-31    0.219999
2000-02-01   -1.021874
2000-02-02   -0.166256
2000-02-03   -1.855476
2000-02-04   -0.538952
2000-02-07   -0.902403
2000-02-08   -0.105402
2000-02-09   -0.935287
2000-02-10    1.261172
2000-02-11    0.869008
Freq: B, Name: A, dtype: float64


df1['A'].shift()

2000-01-03         NaN
2000-01-04   -0.256639
2000-01-05   -0.441481
2000-01-06    0.687317
2000-01-07    0.524671
2000-01-10    1.436570
2000-01-11    0.047872
2000-01-12    0.930656
2000-01-13    0.907508
2000-01-14    0.539813
2000-01-17    0.157760
2000-01-18    0.309461
2000-01-19    0.010488
2000-01-20    1.299948
2000-01-21   -1.287793
2000-01-24   -0.100412
2000-01-25    0.404562
2000-01-26    0.126925
2000-01-27   -0.960476
2000-01-28    2.786106
2000-01-31    1.691881
2000-02-01    0.219999
2000-02-02   -1.021874
2000-02-03   -0.166256
2000-02-04   -1.855476
2000-02-07   -0.538952
2000-02-08   -0.902403
2000-02-09   -0.105402
2000-02-10   -0.935287
2000-02-11    1.261172
Freq: B, Name: A, dtype: float64


a = df1.index.shift() - df1.index

a

TimedeltaIndex(['1 days', '1 days', '1 days', '1 days', '3 days', '1 days',
                '1 days', '1 days', '1 days', '3 days', '1 days', '1 days',
                '1 days', '1 days', '3 days', '1 days', '1 days', '1 days',
                '1 days', '3 days', '1 days', '1 days', '1 days', '1 days',
                '3 days', '1 days', '1 days', '1 days', '1 days', '3 days'],
               dtype='timedelta64[ns]', freq=None)


df1.index.values # index中内部的保存数据类型。包装成index后有对应于index的其他操作

array(['2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000',
       '2000-01-05T00:00:00.000000000', '2000-01-06T00:00:00.000000000',
       '2000-01-07T00:00:00.000000000', '2000-01-10T00:00:00.000000000',
       '2000-01-11T00:00:00.000000000', '2000-01-12T00:00:00.000000000',
       '2000-01-13T00:00:00.000000000', '2000-01-14T00:00:00.000000000',
       '2000-01-17T00:00:00.000000000', '2000-01-18T00:00:00.000000000',
       '2000-01-19T00:00:00.000000000', '2000-01-20T00:00:00.000000000',
       '2000-01-21T00:00:00.000000000', '2000-01-24T00:00:00.000000000',
       '2000-01-25T00:00:00.000000000', '2000-01-26T00:00:00.000000000',
       '2000-01-27T00:00:00.000000000', '2000-01-28T00:00:00.000000000',
       '2000-01-31T00:00:00.000000000', '2000-02-01T00:00:00.000000000',
       '2000-02-02T00:00:00.000000000', '2000-02-03T00:00:00.000000000',
       '2000-02-04T00:00:00.000000000', '2000-02-07T00:00:00.000000000',
       '2000-02-08T00:00:00.000000000', '2000-02-09T00:00:00.000000000',
       '2000-02-10T00:00:00.000000000', '2000-02-11T00:00:00.000000000'],
      dtype='datetime64[ns]')


df1.index.values.to_period()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[90], line 1
----> 1 df1.index.values.to_period()

AttributeError: 'numpy.ndarray' object has no attribute 'to_period'


df1.index.to_period()

PeriodIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
             '2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',
             '2000-01-13', '2000-01-14', '2000-01-17', '2000-01-18',
             '2000-01-19', '2000-01-20', '2000-01-21', '2000-01-24',
             '2000-01-25', '2000-01-26', '2000-01-27', '2000-01-28',
             '2000-01-31', '2000-02-01', '2000-02-02', '2000-02-03',
             '2000-02-04', '2000-02-07', '2000-02-08', '2000-02-09',
             '2000-02-10', '2000-02-11'],
            dtype='period[B]')

df2


df1.index

DatetimeIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
               '2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',
               '2000-01-13', '2000-01-14', '2000-01-17', '2000-01-18',
               '2000-01-19', '2000-01-20', '2000-01-21', '2000-01-24',
               '2000-01-25', '2000-01-26', '2000-01-27', '2000-01-28',
               '2000-01-31', '2000-02-01', '2000-02-02', '2000-02-03',
               '2000-02-04', '2000-02-07', '2000-02-08', '2000-02-09',
               '2000-02-10', '2000-02-11'],
              dtype='datetime64[ns]', freq='B')


df2.index

PeriodIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
             '2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',
             '2000-01-13', '2000-01-14', '2000-01-17', '2000-01-18',
             '2000-01-19', '2000-01-20', '2000-01-21', '2000-01-24',
             '2000-01-25', '2000-01-26', '2000-01-27', '2000-01-28',
             '2000-01-31', '2000-02-01', '2000-02-02', '2000-02-03',
             '2000-02-04', '2000-02-07', '2000-02-08', '2000-02-09',
             '2000-02-10', '2000-02-11'],
            dtype='period[B]')


df2.index.values

array([Period('2000-01-03', 'B'), Period('2000-01-04', 'B'),
       Period('2000-01-05', 'B'), Period('2000-01-06', 'B'),
       Period('2000-01-07', 'B'), Period('2000-01-10', 'B'),
       Period('2000-01-11', 'B'), Period('2000-01-12', 'B'),
       Period('2000-01-13', 'B'), Period('2000-01-14', 'B'),
       Period('2000-01-17', 'B'), Period('2000-01-18', 'B'),
       Period('2000-01-19', 'B'), Period('2000-01-20', 'B'),
       Period('2000-01-21', 'B'), Period('2000-01-24', 'B'),
       Period('2000-01-25', 'B'), Period('2000-01-26', 'B'),
       Period('2000-01-27', 'B'), Period('2000-01-28', 'B'),
       Period('2000-01-31', 'B'), Period('2000-02-01', 'B'),
       Period('2000-02-02', 'B'), Period('2000-02-03', 'B'),
       Period('2000-02-04', 'B'), Period('2000-02-07', 'B'),
       Period('2000-02-08', 'B'), Period('2000-02-09', 'B'),
       Period('2000-02-10', 'B'), Period('2000-02-11', 'B')], dtype=object)


df2.index.week

Int64Index([1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5,
            5, 5, 5, 6, 6, 6, 6, 6],
           dtype='int64')


df2.index.day_of_week

Int64Index([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1,
            2, 3, 4, 0, 1, 2, 3, 4],
           dtype='int64')


df2.index.weekofyear

Int64Index([1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5,
            5, 5, 5, 6, 6, 6, 6, 6],
           dtype='int64')


df2.index.to_timestamp()

DatetimeIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
               '2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',
               '2000-01-13', '2000-01-14', '2000-01-17', '2000-01-18',
               '2000-01-19', '2000-01-20', '2000-01-21', '2000-01-24',
               '2000-01-25', '2000-01-26', '2000-01-27', '2000-01-28',
               '2000-01-31', '2000-02-01', '2000-02-02', '2000-02-03',
               '2000-02-04', '2000-02-07', '2000-02-08', '2000-02-09',
               '2000-02-10', '2000-02-11'],
              dtype='datetime64[ns]', freq='B')


df2.index.shift() - df2.index

Index([<BusinessDay>, <BusinessDay>, <BusinessDay>, <BusinessDay>,
       <BusinessDay>, <BusinessDay>, <BusinessDay>, <BusinessDay>,
       <BusinessDay>, <BusinessDay>, <BusinessDay>, <BusinessDay>,
       <BusinessDay>, <BusinessDay>, <BusinessDay>, <BusinessDay>,
       <BusinessDay>, <BusinessDay>, <BusinessDay>, <BusinessDay>,
       <BusinessDay>, <BusinessDay>, <BusinessDay>, <BusinessDay>,
       <BusinessDay>, <BusinessDay>, <BusinessDay>, <BusinessDay>,
       <BusinessDay>, <BusinessDay>],
      dtype='object')


df2.index[1:].append(pd.Index([np.nan])) # 因为shift直接平移日期。若需直接平移数值，可用这个替代。

PeriodIndex(['2000-01-04', '2000-01-05', '2000-01-06', '2000-01-07',
             '2000-01-10', '2000-01-11', '2000-01-12', '2000-01-13',
             '2000-01-14', '2000-01-17', '2000-01-18', '2000-01-19',
             '2000-01-20', '2000-01-21', '2000-01-24', '2000-01-25',
             '2000-01-26', '2000-01-27', '2000-01-28', '2000-01-31',
             '2000-02-01', '2000-02-02', '2000-02-03', '2000-02-04',
             '2000-02-07', '2000-02-08', '2000-02-09', '2000-02-10',
             '2000-02-11',        'NaT'],
            dtype='period[B]')


df2.index[1:].append(pd.Index([np.nan])).asfreq('D') - df2.index.asfreq('D')

Index([     <Day>,      <Day>,      <Day>,      <Day>, <3 * Days>,      <Day>,
            <Day>,      <Day>,      <Day>, <3 * Days>,      <Day>,      <Day>,
            <Day>,      <Day>, <3 * Days>,      <Day>,      <Day>,      <Day>,
            <Day>, <3 * Days>,      <Day>,      <Day>,      <Day>,      <Day>,
       <3 * Days>,      <Day>,      <Day>,      <Day>,      <Day>,        NaT],
      dtype='object')


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

df


display(df)


temp = pd.read_csv('../../../data/idx_df_csv.csv',dtype={'ticker':str})
temp.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1365 entries, 0 to 1364
Data columns (total 9 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   ticker         1365 non-null   object 
 1   secShortName   1365 non-null   object 
 2   tradeDate      1365 non-null   object 
 3   preCloseIndex  1365 non-null   float64
 4   openIndex      1365 non-null   float64
 5   highestIndex   1365 non-null   float64
 6   lowestIndex    1365 non-null   float64
 7   closeIndex     1365 non-null   float64
 8   turnoverVol    1365 non-null   int64  
dtypes: float64(5), int64(1), object(3)
memory usage: 96.1+ KB


temp


# 如果读取csv或其他类型的文件, 可能会遇到一些格式转换的问题
pd.read_csv('../../../data/idx_df_csv.csv')


df.head(10)


df.tail()


df.describe()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5838750 entries, 0 to 5838749
Data columns (total 12 columns):
 #   Column          Dtype  
---  ------          -----  
 0   secID           object 
 1   secShortName    object 
 2   exchangeCD      object 
 3   tradeDate       object 
 4   preClosePrice   float64
 5   closePrice      float64
 6   turnoverVol     int64  
 7   turnoverValue   float64
 8   dealAmount      float64
 9   turnoverRate    float64
 10  negMarketValue  float64
 11  marketValue     float64
dtypes: float64(7), int64(1), object(4)
memory usage: 534.6+ MB


df['secShortName'].nunique() # 多少只股票

4853


df['turnoverValue'].mean()

200899200.96656865


df.select_dtypes(include=np.number)


df.select_dtypes(include=np.number).mean()

preClosePrice     1.368568e+02
closePrice        1.368763e+02
turnoverVol       5.525219e+06
turnoverValue     2.008992e+08
dealAmount        1.078089e+04
turnoverRate      2.986831e-02
negMarketValue    1.324705e+10
marketValue       1.835989e+10
dtype: float64


df.select_dtypes(include=np.number).mean(axis=1)

0          4.314767e+10
1          4.237068e+10
2          4.147094e+10
3          4.002552e+10
4          4.055811e+10
               ...     
5838745    4.227324e+07
5838746    4.214321e+07
5838747    4.166574e+07
5838748    4.199687e+07
5838749    4.207461e+07
Length: 5838750, dtype: float64


df['secShortName']=='平安银行'

0           True
1           True
2           True
3           True
4           True
           ...  
5838745    False
5838746    False
5838747    False
5838748    False
5838749    False
Name: secShortName, Length: 5838750, dtype: bool


df_pingan = df[df['secShortName']=='平安银行'].copy()


df_pingan


x = np.array(['Testing', 'a', 'string'], dtype='|S7') # 长度为7的字符串。|是byteorder指示符
y = np.array(['Testing', 'a', 'string'], dtype=object)


x[1] = 'a really really really long string'
x

array([b'Testing', b'a reall', b'string'], dtype='|S7')


y[1] = 'a really really really long string'
y

array(['Testing', 'a really really really long string', 'string'],
      dtype=object)


temp = pd.DataFrame({'a':range(5)})


temp['a'].dtype

dtype('int64')


temp['a'].astype(object).dtype

dtype('O')


temp['a'].astype(str).dtype

dtype('O')


temp = tm.makeTimeDataFrame(3)
display(temp)


temp2 = temp.stack().to_frame().reset_index()
temp2.columns = ['date','variable','value']


temp2


temp2.sort_values(by='variable', inplace=True)


temp2


temp2.sort_values(by='variable',ascending=False)


temp2


temp2.duplicated('variable')

0     False
4      True
8      True
1     False
5      True
9      True
2     False
6      True
10     True
3     False
7      True
11     True
dtype: bool


temp2.drop_duplicates('variable')


temp2.drop_duplicates('variable',keep='last')


temp = tm.makeDataFrame()
temp['A'] = np.random.randint(0, 100, size=temp.shape[0])


temp.drop(['B','C','D'],axis=1, inplace=True)


temp.columns = ['score']


temp


?pd.cut

Signature:
pd.cut(
    x,
    bins,
    right: 'bool' = True,
    labels=None,
    retbins: 'bool' = False,
    precision: 'int' = 3,
    include_lowest: 'bool' = False,
    duplicates: 'str' = 'raise',
    ordered: 'bool' = True,
)
Docstring:
Bin values into discrete intervals.

Use `cut` when you need to segment and sort data values into bins. This
function is also useful for going from a continuous variable to a
categorical variable. For example, `cut` could convert ages to groups of
age ranges. Supports binning into an equal number of bins, or a
pre-specified array of bins.

Parameters
----------
x : array-like
    The input array to be binned. Must be 1-dimensional.
bins : int, sequence of scalars, or IntervalIndex
    The criteria to bin by.

    * int : Defines the number of equal-width bins in the range of `x`. The
      range of `x` is extended by .1% on each side to include the minimum
      and maximum values of `x`.
    * sequence of scalars : Defines the bin edges allowing for non-uniform
      width. No extension of the range of `x` is done.
    * IntervalIndex : Defines the exact bins to be used. Note that
      IntervalIndex for `bins` must be non-overlapping.

right : bool, default True
    Indicates whether `bins` includes the rightmost edge or not. If
    ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
    indicate (1,2], (2,3], (3,4]. This argument is ignored when
    `bins` is an IntervalIndex.
labels : array or False, default None
    Specifies the labels for the returned bins. Must be the same length as
    the resulting bins. If False, returns only integer indicators of the
    bins. This affects the type of the output container (see below).
    This argument is ignored when `bins` is an IntervalIndex. If True,
    raises an error. When `ordered=False`, labels must be provided.
retbins : bool, default False
    Whether to return the bins or not. Useful when bins is provided
    as a scalar.
precision : int, default 3
    The precision at which to store and display the bins labels.
include_lowest : bool, default False
    Whether the first interval should be left-inclusive or not.
duplicates : {default 'raise', 'drop'}, optional
    If bin edges are not unique, raise ValueError or drop non-uniques.
ordered : bool, default True
    Whether the labels are ordered or not. Applies to returned types
    Categorical and Series (with Categorical dtype). If True,
    the resulting categorical will be ordered. If False, the resulting
    categorical will be unordered (labels must be provided).

    .. versionadded:: 1.1.0

Returns
-------
out : Categorical, Series, or ndarray
    An array-like object representing the respective bin for each value
    of `x`. The type depends on the value of `labels`.

    * None (default) : returns a Series for Series `x` or a
      Categorical for all other inputs. The values stored within
      are Interval dtype.

    * sequence of scalars : returns a Series for Series `x` or a
      Categorical for all other inputs. The values stored within
      are whatever the type in the sequence is.

    * False : returns an ndarray of integers.

bins : numpy.ndarray or IntervalIndex.
    The computed or specified bins. Only returned when `retbins=True`.
    For scalar or sequence `bins`, this is an ndarray with the computed
    bins. If set `duplicates=drop`, `bins` will drop non-unique bin. For
    an IntervalIndex `bins`, this is equal to `bins`.

See Also
--------
qcut : Discretize variable into equal-sized buckets based on rank
    or based on sample quantiles.
Categorical : Array type for storing data that come from a
    fixed set of values.
Series : One-dimensional array with axis labels (including time series).
IntervalIndex : Immutable Index implementing an ordered, sliceable set.

Notes
-----
Any NA values will be NA in the result. Out of bounds values will be NA in
the resulting Series or Categorical object.

Reference :ref:`the user guide <reshaping.tile.cut>` for more examples.

Examples
--------
Discretize into three equal-sized bins.

>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3)
... # doctest: +ELLIPSIS
[(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...

>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3, retbins=True)
... # doctest: +ELLIPSIS
([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
array([0.994, 3.   , 5.   , 7.   ]))

Discovers the same bins, but assign them specific labels. Notice that
the returned Categorical's categories are `labels` and is ordered.

>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]),
...        3, labels=["bad", "medium", "good"])
['bad', 'good', 'medium', 'medium', 'good', 'bad']
Categories (3, object): ['bad' < 'medium' < 'good']

``ordered=False`` will result in unordered categories when labels are passed.
This parameter can be used to allow non-unique labels:

>>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3,
...        labels=["B", "A", "B"], ordered=False)
['B', 'B', 'A', 'A', 'B', 'B']
Categories (2, object): ['A', 'B']

``labels=False`` implies you just want the bins back.

>>> pd.cut([0, 1, 1, 2], bins=4, labels=False)
array([0, 1, 1, 3])

Passing a Series as an input returns a Series with categorical dtype:

>>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
...               index=['a', 'b', 'c', 'd', 'e'])
>>> pd.cut(s, 3)
... # doctest: +ELLIPSIS
a    (1.992, 4.667]
b    (1.992, 4.667]
c    (4.667, 7.333]
d     (7.333, 10.0]
e     (7.333, 10.0]
dtype: category
Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ...

Passing a Series as an input returns a Series with mapping value.
It is used to map numerically to intervals based on bins.

>>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
...               index=['a', 'b', 'c', 'd', 'e'])
>>> pd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)
... # doctest: +ELLIPSIS
(a    1.0
 b    2.0
 c    3.0
 d    4.0
 e    NaN
 dtype: float64,
 array([ 0,  2,  4,  6,  8, 10]))

Use `drop` optional when bins is not unique

>>> pd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,
...        right=False, duplicates='drop')
... # doctest: +ELLIPSIS
(a    1.0
 b    2.0
 c    3.0
 d    3.0
 e    NaN
 dtype: float64,
 array([ 0,  2,  4,  6, 10]))

Passing an IntervalIndex for `bins` results in those categories exactly.
Notice that values not covered by the IntervalIndex are set to NaN. 0
is to the left of the first bin (which is closed on the right), and 1.5
falls between two bins.

>>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
>>> pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)
[NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]]
Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]]
File:      ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/reshape/tile.py
Type:      function


temp['score'][3] = 0


bins = [0, 25, 50, 75, 100]
pd.cut(temp['score'], bins=bins, labels=['D','C','B','A'])

ocSZYYUkHs      B
gTKl4LEzN5      B
E3WFBpPnP1      B
l7t7hYzIjN    NaN
5MyJDWBGFj      A
V9PGZpnzu1      D
HHopAhwz1P      D
H8aIGxvWJP      C
yAE3gfP5fn      B
D3SfsS2ddm      D
EjwMd3huy1      A
SEsr3gTI6C      A
StlWk4TtWt      D
lJ99Pqv1zg      C
Eeoc8MbIok      A
pkJofTFlsG      A
kpbtUkTTTy      B
6K6nnrZZAz    NaN
pZyED2L9lP      D
5CF9jssPwu      C
5jiQoqwnOp      A
wFwwHYb4x0      D
humbKxNMFR      B
oC1e0WXbvX      D
PumV2ro6sp      D
KZu0LmKlTN      D
2JPkKXlSWj      A
JEcnqKLoo0      C
BDMmStdXla      A
jeoVnXIIgq      D
Name: score, dtype: category
Categories (4, object): ['D' < 'C' < 'B' < 'A']


bins = [0, 25, 50, 75, 100]
pd.cut(temp['score'], bins=bins, labels=['D','C','B','A'],include_lowest=True)

ocSZYYUkHs    B
gTKl4LEzN5    B
E3WFBpPnP1    B
l7t7hYzIjN    D
5MyJDWBGFj    A
V9PGZpnzu1    D
HHopAhwz1P    D
H8aIGxvWJP    C
yAE3gfP5fn    B
D3SfsS2ddm    D
EjwMd3huy1    A
SEsr3gTI6C    A
StlWk4TtWt    D
lJ99Pqv1zg    C
Eeoc8MbIok    A
pkJofTFlsG    A
kpbtUkTTTy    B
6K6nnrZZAz    D
pZyED2L9lP    D
5CF9jssPwu    C
5jiQoqwnOp    A
wFwwHYb4x0    D
humbKxNMFR    B
oC1e0WXbvX    D
PumV2ro6sp    D
KZu0LmKlTN    D
2JPkKXlSWj    A
JEcnqKLoo0    C
BDMmStdXla    A
jeoVnXIIgq    D
Name: score, dtype: category
Categories (4, object): ['D' < 'C' < 'B' < 'A']


df_pingan


df_pingan.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1705 entries, 0 to 1704
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   secID           1705 non-null   object 
 1   secShortName    1705 non-null   object 
 2   exchangeCD      1705 non-null   object 
 3   tradeDate       1705 non-null   object 
 4   preClosePrice   1705 non-null   float64
 5   closePrice      1705 non-null   float64
 6   turnoverVol     1705 non-null   int64  
 7   turnoverValue   1705 non-null   float64
 8   dealAmount      1705 non-null   float64
 9   turnoverRate    1705 non-null   float64
 10  negMarketValue  1705 non-null   float64
 11  marketValue     1705 non-null   float64
dtypes: float64(7), int64(1), object(4)
memory usage: 173.2+ KB


df_pingan['tradeDate'] = pd.to_datetime(df_pingan['tradeDate'], format='%Y-%m-%d')


df_pingan.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1705 entries, 0 to 1704
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype         
---  ------          --------------  -----         
 0   secID           1705 non-null   object        
 1   secShortName    1705 non-null   object        
 2   exchangeCD      1705 non-null   object        
 3   tradeDate       1705 non-null   datetime64[ns]
 4   preClosePrice   1705 non-null   float64       
 5   closePrice      1705 non-null   float64       
 6   turnoverVol     1705 non-null   int64         
 7   turnoverValue   1705 non-null   float64       
 8   dealAmount      1705 non-null   float64       
 9   turnoverRate    1705 non-null   float64       
 10  negMarketValue  1705 non-null   float64       
 11  marketValue     1705 non-null   float64       
dtypes: datetime64[ns](1), float64(7), int64(1), object(3)
memory usage: 173.2+ KB


df_pingan.set_index('tradeDate', inplace=True)


df_pingan


df_pingan.resample('BM').last()


df_pingan.resample('M').mean(numeric_only=True)


df_pingan.loc['2015']


df_pingan.loc['2015-01':'2018-03']

df


df.select_dtypes(np.number).apply(lambda x: x**2+3)


df.groupby('secShortName')

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f8c8674a3a0>


df.groupby('secShortName').last()


df.groupby('secShortName').first()


df.groupby('secShortName').head(3)


df.groupby('secShortName').nth(-2)


arrays = [
    np.array(["bar", "bar", 'bar', "baz", "baz", "baz", "foo", "foo", "foo", "qux", "qux", "qux"]),
    np.array([1,2,3, 1,2,3, 1,2,3, 1,2,3]),
]
temp = pd.DataFrame(np.random.randn(12, 4), index=arrays)


temp


temp.loc[('bar',2):('foo',3)]


df['tradeDate'] = pd.to_datetime(df['tradeDate'], format='%Y-%m-%d')


df.set_index(['secShortName','tradeDate'])


df.set_index(['secShortName','tradeDate'],inplace=True)

df


df.groupby(['secShortName','tradeDate']).last()


df.loc['平安银行','2021']

/var/folders/gh/26025ywx7w128zfds279s_9r0000gn/T/ipykernel_73497/224712730.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
  df.loc['平安银行','2021']
/var/folders/gh/26025ywx7w128zfds279s_9r0000gn/T/ipykernel_73497/224712730.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
  df.loc['平安银行','2021']


df.loc[('平安银行','2021')] # a tuple is interpreted as one multi-level key

/var/folders/gh/26025ywx7w128zfds279s_9r0000gn/T/ipykernel_73497/2348492628.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
  df.loc[('平安银行','2021')] # a tuple is interpreted as one multi-level key
/var/folders/gh/26025ywx7w128zfds279s_9r0000gn/T/ipykernel_73497/2348492628.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
  df.loc[('平安银行','2021')] # a tuple is interpreted as one multi-level key


df.loc[['平安银行','2016']]# a list is used to specify several keys [on the same level]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[173], line 1
----> 1 df.loc[['平安银行','2016']]

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1073, in _LocationIndexer.__getitem__(self, key)
   1070 axis = self.axis or 0
   1072 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1073 return self._getitem_axis(maybe_callable, axis=axis)

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1301, in _LocIndexer._getitem_axis(self, key, axis)
   1298     if hasattr(key, "ndim") and key.ndim > 1:
   1299         raise ValueError("Cannot index with multidimensional key")
-> 1301     return self._getitem_iterable(key, axis=axis)
   1303 # nested tuple slicing
   1304 if is_nested_tuple(key, labels):

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1239, in _LocIndexer._getitem_iterable(self, key, axis)
   1236 self._validate_key(key, axis)
   1238 # A collection of keys
-> 1239 keyarr, indexer = self._get_listlike_indexer(key, axis)
   1240 return self.obj._reindex_with_indexers(
   1241     {axis: [keyarr, indexer]}, copy=True, allow_dups=True
   1242 )

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1432, in _LocIndexer._get_listlike_indexer(self, key, axis)
   1429 ax = self.obj._get_axis(axis)
   1430 axis_name = self.obj._get_axis_name(axis)
-> 1432 keyarr, indexer = ax._get_indexer_strict(key, axis_name)
   1434 return keyarr, indexer

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2623, in MultiIndex._get_indexer_strict(self, key, axis_name)
   2620 if len(keyarr) and not isinstance(keyarr[0], tuple):
   2621     indexer = self._get_indexer_level_0(keyarr)
-> 2623     self._raise_if_missing(key, indexer, axis_name)
   2624     return self[indexer], indexer
   2626 return super()._get_indexer_strict(key, axis_name)

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2641, in MultiIndex._raise_if_missing(self, key, indexer, axis_name)
   2639 cmask = check == -1
   2640 if cmask.any():
-> 2641     raise KeyError(f"{keyarr[cmask]} not in index")
   2642 # We get here when levels still contain values which are not
   2643 # actually in Index anymore
   2644 raise KeyError(f"{keyarr} not in index")

KeyError: "['2016'] not in index"


df.loc[('平安银行','2015':'2016')]

  Cell In[174], line 1
    df.loc[('平安银行','2015':'2016')]
                         ^
SyntaxError: invalid syntax


df.loc[('平安银行',pd.Timestamp('2015-01-05')):('平安银行', pd.Timestamp('2015-01-28'))]

---------------------------------------------------------------------------
UnsortedIndexError                        Traceback (most recent call last)
Cell In[175], line 1
----> 1 df.loc[('平安银行',pd.Timestamp('2015-01-05')):('平安银行', pd.Timestamp('2015-01-28'))]

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1073, in _LocationIndexer.__getitem__(self, key)
   1070 axis = self.axis or 0
   1072 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1073 return self._getitem_axis(maybe_callable, axis=axis)

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1290, in _LocIndexer._getitem_axis(self, key, axis)
   1288 if isinstance(key, slice):
   1289     self._validate_key(key, axis)
-> 1290     return self._get_slice_axis(key, axis=axis)
   1291 elif com.is_bool_indexer(key):
   1292     return self._getbool_axis(key, axis=axis)

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1324, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
   1321     return obj.copy(deep=False)
   1323 labels = obj._get_axis(axis)
-> 1324 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
   1326 if isinstance(indexer, slice):
   1327     return self.obj._slice(indexer, axis=axis)

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6602, in Index.slice_indexer(self, start, end, step, kind)
   6559 """
   6560 Compute the slice indexer for input labels and step.
   6561 
   (...)
   6598 slice(1, 3, None)
   6599 """
   6600 self._deprecated_arg(kind, "kind", "slice_indexer")
-> 6602 start_slice, end_slice = self.slice_locs(start, end, step=step)
   6604 # return a slice
   6605 if not is_scalar(start_slice):

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2775, in MultiIndex.slice_locs(self, start, end, step, kind)
   2772 self._deprecated_arg(kind, "kind", "slice_locs")
   2773 # This function adds nothing to its parent implementation (the magic
   2774 # happens in get_slice_bound method), but it adds meaningful doc.
-> 2775 return super().slice_locs(start, end, step)

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6810, in Index.slice_locs(self, start, end, step, kind)
   6808 start_slice = None
   6809 if start is not None:
-> 6810     start_slice = self.get_slice_bound(start, "left")
   6811 if start_slice is None:
   6812     start_slice = 0

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2714, in MultiIndex.get_slice_bound(self, label, side, kind)
   2712 if not isinstance(label, tuple):
   2713     label = (label,)
-> 2714 return self._partial_tup_index(label, side=side)

File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2779, in MultiIndex._partial_tup_index(self, tup, side)
   2777 def _partial_tup_index(self, tup: tuple, side: Literal["left", "right"] = "left"):
   2778     if len(tup) > self._lexsort_depth:
-> 2779         raise UnsortedIndexError(
   2780             f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth "
   2781             f"({self._lexsort_depth})"
   2782         )
   2784     n = len(tup)
   2785     start, end = 0, len(self)

UnsortedIndexError: 'Key length (2) was greater than MultiIndex lexsort depth (0)'


df2 = df.sort_index() # 必须先排序才可做slicing


df2.loc[('平安银行',pd.Timestamp('2015-01-05')):('平安银行', pd.Timestamp('2015-01-28'))]


df2.loc[pd.IndexSlice[('宁德时代','2015-01'):('平安银行','2020-02')]]


df.loc['平安银行'].loc['2015':'2016']


df1 = pd.DataFrame({'a':[1,2,3],'b':[4,5,6],'c':[10,20,30]})
df2 = pd.DataFrame({'a':np.array([1,2,6]),
                    'b':[s for s in 'abc'],
                    'd':[s for s in 'zxz']})

df1

df2


pd.merge(df1,df2,on='a')


pd.merge(df1,df2,on='a',how='outer')


pd.merge(df1,df2,on='a',how='left')


pd.merge(df1,df2,on='a',how='right')


temp = tm.makeTimeDataFrame(3)
display(temp)


temp.stack().to_frame().reset_index()


pd.DataFrame(temp.stack()).reset_index()


temp2 = pd.DataFrame(temp.stack()).reset_index()
temp2.columns=['date','variable','value']


temp2


def unpivot(frame):
    """
    Essentially the same as 'stack'
    """
    N, K = frame.shape
    data = {
        "value": frame.to_numpy().ravel("F"),
        "variable": np.asarray(frame.columns).repeat(N),
        "date": np.tile(np.asarray(frame.index), K),
    }
    return pd.DataFrame(data, columns=["date", "variable", "value"])


temp2 = unpivot(temp)


temp2


temp2.set_index(['date','variable'])


temp2.set_index(['date','variable']).unstack()


temp2


temp2.pivot(index='date',columns='variable',values='value')


temp2['value2'] = temp2['value']**2


temp2


temp2.pivot(index='date',columns='variable')


df.reset_index()


df.reset_index(inplace=True)

df


df.pivot(index='tradeDate',columns='secID',values='closePrice').iloc[:,0:4]


df.reset_index().pivot(index='tradeDate',columns='secID',values='closePrice').iloc[:,0:4].plot()

<AxesSubplot: xlabel='tradeDate'>


import matplotlib as mpl
import matplotlib.pyplot as plt


fig = plt.figure()  # an empty figure with no Axes
fig, ax = plt.subplots()  # a figure with a single Axes
fig, axs = plt.subplots(2, 2)  # a figure with a 2x2 grid of Axes

<Figure size 640x480 with 0 Axes>


fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])

[<matplotlib.lines.Line2D at 0x7f8bc80cae20>]


temp2


fig, axs = plt.subplots(1,2)
x = range(temp2.shape[0])
y1 = temp2['value']
y2 = temp2['value']**2
axs[0].plot(x, y1)
axs[1].plot(x, y2)
axs[0].plot(x, y1**3)
axs[1].plot(x, y2-y1)

[<matplotlib.lines.Line2D at 0x7f8bc837ba60>]


x = pd.date_range(start='2021-01-03',periods=temp2.shape[0])
plt.plot(x,temp2['value'], label='random value')
plt.plot(x,temp2['value']**2, label='quadratic of random value')
plt.xlabel('date')
plt.ylabel('value')
plt.title('Example')
plt.legend()

<matplotlib.legend.Legend at 0x7f8bc8111a30>


df_pingan.select_dtypes(np.number).corr()


corr = df_pingan.select_dtypes(np.number).corr()


corr


np.ones_like?

Signature: np.ones_like(a, dtype=None, order='K', subok=True, shape=None)
Docstring:
Return an array of ones with the same shape and type as a given array.

Parameters
----------
a : array_like
    The shape and data-type of `a` define these same attributes of
    the returned array.
dtype : data-type, optional
    Overrides the data type of the result.

    .. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
    Overrides the memory layout of the result. 'C' means C-order,
    'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
    'C' otherwise. 'K' means match the layout of `a` as closely
    as possible.

    .. versionadded:: 1.6.0
subok : bool, optional.
    If True, then the newly created array will use the sub-class
    type of `a`, otherwise it will be a base-class array. Defaults
    to True.
shape : int or sequence of ints, optional.
    Overrides the shape of the result. If order='K' and the number of
    dimensions is unchanged, will try to keep order, otherwise,
    order='C' is implied.

    .. versionadded:: 1.17.0

Returns
-------
out : ndarray
    Array of ones with the same shape and type as `a`.

See Also
--------
empty_like : Return an empty array with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full_like : Return a new array with shape of input filled with value.
ones : Return a new array setting values to one.

Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.ones_like(x)
array([[1, 1, 1],
       [1, 1, 1]])

>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.ones_like(y)
array([1.,  1.,  1.])
File:      ~/opt/anaconda3/lib/python3.9/site-packages/numpy/core/numeric.py
Type:      function


np.ones_like(corr, dtype=bool)

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


np.triu?

Signature: np.triu(m, k=0)
Docstring:
Upper triangle of an array.

Return a copy of an array with the elements below the `k`-th diagonal
zeroed. For arrays with ``ndim`` exceeding 2, `triu` will apply to the
final two axes.

Please refer to the documentation for `tril` for further details.

See Also
--------
tril : lower triangle of an array

Examples
--------
>>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 0,  8,  9],
       [ 0,  0, 12]])

>>> np.triu(np.arange(3*4*5).reshape(3, 4, 5))
array([[[ 0,  1,  2,  3,  4],
        [ 0,  6,  7,  8,  9],
        [ 0,  0, 12, 13, 14],
        [ 0,  0,  0, 18, 19]],
       [[20, 21, 22, 23, 24],
        [ 0, 26, 27, 28, 29],
        [ 0,  0, 32, 33, 34],
        [ 0,  0,  0, 38, 39]],
       [[40, 41, 42, 43, 44],
        [ 0, 46, 47, 48, 49],
        [ 0,  0, 52, 53, 54],
        [ 0,  0,  0, 58, 59]]])
File:      ~/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/twodim_base.py
Type:      function


# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))


mask

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


import seaborn as sns


# Set up the matplotlib figure
fig, ax = plt.subplots(figsize=(10, 10))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, annot=True,
            square=True, linewidths=.5, annot_kws={"size": 10},cbar_kws={"shrink": .5})

<AxesSubplot: >


df_pingan_num = df_pingan.select_dtypes(np.number)


df_pingan_num


df_pingan_num.plot()

<AxesSubplot: xlabel='tradeDate'>


plt.rcParams['figure.figsize'] = (16.0, 9.0)


df_pingan_num.drop(['negMarketValue','marketValue'],axis=1).plot()

<AxesSubplot: xlabel='tradeDate'>


def standardization(col):
    return (col - col.mean()) / col.std()


df_pingan_num.apply(standardization).plot()

<AxesSubplot: xlabel='tradeDate'>


df_pingan_num.apply(standardization)[['preClosePrice','turnoverValue','marketValue']].plot(style=['bs-', 'ro-', 'y^-'])

<AxesSubplot: xlabel='tradeDate'>


df_pingan_num.plot(subplots=True)

array([<AxesSubplot: xlabel='tradeDate'>,
       <AxesSubplot: xlabel='tradeDate'>,
       <AxesSubplot: xlabel='tradeDate'>,
       <AxesSubplot: xlabel='tradeDate'>,
       <AxesSubplot: xlabel='tradeDate'>,
       <AxesSubplot: xlabel='tradeDate'>,
       <AxesSubplot: xlabel='tradeDate'>,
       <AxesSubplot: xlabel='tradeDate'>], dtype=object)

	A	B	C	D
cZJYRFBjsZ	1.017466	0.408885	0.322214	0.054719
lLVrbjR6Vr	0.033257	0.616884	-1.040222	-1.780187
eLUp4PALTt	2.656438	0.640599	0.261727	-0.520845
BzoGUejyQV	-0.828406	0.037566	-0.536611	0.292041
UfVsUDv70f	-0.563905	-0.634545	0.429127	0.117443
ZCvtoS4VLQ	0.455937	-1.760947	0.924035	-1.096260
ZCnKayvc0L	1.224438	-0.215123	-0.083788	-0.436338
trYE7TVe9q	-0.316089	0.210430	1.309266	-0.360450
0BjFKt4A4R	0.878868	0.925385	-1.945032	0.580221
g5R8etUU47	-1.324358	0.879758	-0.075207	-1.033363
WyUH5ihfxA	-0.505695	-0.562268	-1.301524	-2.136327
lgaFDVh71E	-2.878787	-2.156321	-0.753986	1.287103
MKe0DW9Aco	0.875395	0.718449	0.098643	1.016631
Fu5VORq3HB	0.196059	-1.715751	0.377394	-0.425211
4awpGNwhCr	-1.430431	1.107052	0.351579	0.038333
abWuxFTgak	1.134595	-0.654446	-1.780598	0.475640
LdGjWdsrap	1.052956	-0.321872	1.515367	0.178291
haSxy2J3H7	0.382825	1.793814	0.452140	-0.396571
QKkLEY5HdO	-1.091404	1.648727	1.189487	0.734657
Na4icA12zD	1.463891	0.415739	1.857051	-0.437692
Xrx5Xz8pHm	1.010493	0.532272	0.981934	-1.179908
eXHd43VLjJ	0.301460	-0.344703	0.725504	-0.505409
i0bUrWpwuC	0.818570	1.907338	1.997379	-0.226253
jBFWDXzj9J	1.070393	0.019434	-2.291473	0.049770
eoQ1Xto9xt	1.038056	0.386739	-1.145852	-0.691260
MX1ffAFzk5	1.050974	-0.011987	1.075066	0.700000
v1bcFWTZHT	1.132417	-0.014813	0.277577	0.020498
SrJSnSQKf6	2.308104	0.008408	-0.900696	0.515424
uH7sGOyWjP	-0.144934	-0.138548	0.331670	-0.315515
dHDDaETSMu	0.352889	-0.455680	1.487833	1.291541

	A	B	C	D
2000-01-03	-0.256639	-0.360026	0.781808	2.365876
2000-01-04	-0.441481	-0.416982	-0.085862	1.215792
2000-01-05	0.687317	-0.840725	0.368144	-0.604941
2000-01-06	0.524671	0.562214	1.348964	-0.017417
2000-01-07	1.436570	-1.263538	-0.035792	0.671226
2000-01-10	0.047872	0.716265	0.640712	-1.106825
2000-01-11	0.930656	-1.086892	-0.519984	-0.337252
2000-01-12	0.907508	-0.841800	1.025432	-0.862827
2000-01-13	0.539813	1.520450	0.024358	1.493062
2000-01-14	0.157760	-0.282024	-0.499526	-0.562860
2000-01-17	0.309461	-2.399618	-0.353253	1.401097
2000-01-18	0.010488	-0.879997	-0.551815	-0.522194
2000-01-19	1.299948	0.264738	-0.883126	0.356829
2000-01-20	-1.287793	-0.818199	-1.101668	-0.558555
2000-01-21	-0.100412	0.177467	1.180741	0.013179
2000-01-24	0.404562	0.633419	-1.084324	0.124110
2000-01-25	0.126925	0.241513	-0.769587	-1.072740
2000-01-26	-0.960476	0.935380	-0.443059	-0.887785
2000-01-27	2.786106	-1.733085	-0.935420	1.842912
2000-01-28	1.691881	0.806902	-0.520169	-1.187983
2000-01-31	0.219999	-0.523229	0.483812	-1.892425
2000-02-01	-1.021874	-1.009266	-1.116588	-0.104468
2000-02-02	-0.166256	0.266004	-0.616978	-0.215507
2000-02-03	-1.855476	0.295577	1.180183	-0.084075
2000-02-04	-0.538952	-0.350280	0.007614	-0.075128
2000-02-07	-0.902403	0.642015	1.659144	-1.720960
2000-02-08	-0.105402	1.135322	-1.378690	-2.452588
2000-02-09	-0.935287	0.179174	0.662516	0.180056
2000-02-10	1.261172	-1.363582	2.425621	-0.272038
2000-02-11	0.869008	-2.175696	1.096948	0.191190

	A	B	C	D
2000-01-03	-1.639489	0.871156	0.146877	-1.838182
2000-01-04	2.045404	-2.275963	-2.061878	-0.209553
2000-01-05	0.211661	-0.511191	0.584097	-0.129640
2000-01-06	-0.455644	-0.611017	-1.114282	0.336684
2000-01-07	0.637326	0.002655	1.198229	0.026967
2000-01-10	-1.052640	-1.227736	0.296380	-1.068391
2000-01-11	-1.805982	0.129988	0.302069	0.149396
2000-01-12	-0.800961	1.510763	1.023772	0.485134
2000-01-13	-0.771532	0.966103	0.538022	0.698567
2000-01-14	0.430516	-1.960807	-0.256653	-0.808380
2000-01-17	1.768485	0.076938	-0.182843	-0.487593
2000-01-18	-0.152704	1.178684	1.561573	1.296492
2000-01-19	0.457053	0.015916	0.216055	-0.940051
2000-01-20	-0.097853	1.026431	-0.201932	0.009988
2000-01-21	-0.299978	0.980998	-0.674989	0.110689
2000-01-24	-0.043211	-0.917959	-0.356359	0.704586
2000-01-25	1.284588	1.653408	-1.578361	0.569167
2000-01-26	-0.826600	0.007350	-1.336029	1.181770
2000-01-27	0.035391	-1.108028	1.219294	0.131281
2000-01-28	0.477427	-1.605746	-1.041161	-1.128379
2000-01-31	-0.955470	-0.491113	-0.738858	0.276953
2000-02-01	0.259385	0.335096	-0.485912	-0.007712
2000-02-02	-0.237599	-1.375179	-1.198458	-0.662935
2000-02-03	-0.598414	-0.261143	0.173009	-0.541031
2000-02-04	-1.290128	-1.422936	-2.372973	0.367791
2000-02-07	-0.716938	-1.103327	-0.906131	0.756418
2000-02-08	0.092658	0.755122	0.156270	-0.231200
2000-02-09	-0.860367	0.696204	0.346518	-0.347244
2000-02-10	-0.819556	-0.249229	0.136163	0.666071
2000-02-11	-0.133716	1.038838	-2.515921	1.147261

	A	B	C	D
2000-01-03	-0.256639	-0.360026	0.781808	2.365876
2000-01-04	-0.441481	-0.416982	-0.085862	1.215792
2000-01-05	0.687317	-0.840725	0.368144	-0.604941
2000-01-06	0.524671	0.562214	1.348964	-0.017417
2000-01-07	1.436570	-1.263538	-0.035792	0.671226
2000-01-10	0.047872	0.716265	0.640712	-1.106825
2000-01-11	0.930656	-1.086892	-0.519984	-0.337252
2000-01-12	0.907508	-0.841800	1.025432	-0.862827
2000-01-13	0.539813	1.520450	0.024358	1.493062
2000-01-14	0.157760	-0.282024	-0.499526	-0.562860
2000-01-17	0.309461	-2.399618	-0.353253	1.401097
2000-01-18	0.010488	-0.879997	-0.551815	-0.522194
2000-01-19	1.299948	0.264738	-0.883126	0.356829
2000-01-20	-1.287793	-0.818199	-1.101668	-0.558555
2000-01-21	-0.100412	0.177467	1.180741	0.013179
2000-01-24	0.404562	0.633419	-1.084324	0.124110
2000-01-25	0.126925	0.241513	-0.769587	-1.072740
2000-01-26	-0.960476	0.935380	-0.443059	-0.887785
2000-01-27	2.786106	-1.733085	-0.935420	1.842912
2000-01-28	1.691881	0.806902	-0.520169	-1.187983
2000-01-31	0.219999	-0.523229	0.483812	-1.892425
2000-02-01	-1.021874	-1.009266	-1.116588	-0.104468
2000-02-02	-0.166256	0.266004	-0.616978	-0.215507
2000-02-03	-1.855476	0.295577	1.180183	-0.084075
2000-02-04	-0.538952	-0.350280	0.007614	-0.075128
2000-02-07	-0.902403	0.642015	1.659144	-1.720960
2000-02-08	-0.105402	1.135322	-1.378690	-2.452588
2000-02-09	-0.935287	0.179174	0.662516	0.180056
2000-02-10	1.261172	-1.363582	2.425621	-0.272038
2000-02-11	0.869008	-2.175696	1.096948	0.191190

	A	B	C	D
2000-01-03	-1.639489	0.871156	0.146877	-1.838182
2000-01-04	2.045404	-2.275963	-2.061878	-0.209553
2000-01-05	0.211661	-0.511191	0.584097	-0.129640
2000-01-06	-0.455644	-0.611017	-1.114282	0.336684
2000-01-07	0.637326	0.002655	1.198229	0.026967
2000-01-10	-1.052640	-1.227736	0.296380	-1.068391
2000-01-11	-1.805982	0.129988	0.302069	0.149396
2000-01-12	-0.800961	1.510763	1.023772	0.485134
2000-01-13	-0.771532	0.966103	0.538022	0.698567
2000-01-14	0.430516	-1.960807	-0.256653	-0.808380
2000-01-17	1.768485	0.076938	-0.182843	-0.487593
2000-01-18	-0.152704	1.178684	1.561573	1.296492
2000-01-19	0.457053	0.015916	0.216055	-0.940051
2000-01-20	-0.097853	1.026431	-0.201932	0.009988
2000-01-21	-0.299978	0.980998	-0.674989	0.110689
2000-01-24	-0.043211	-0.917959	-0.356359	0.704586
2000-01-25	1.284588	1.653408	-1.578361	0.569167
2000-01-26	-0.826600	0.007350	-1.336029	1.181770
2000-01-27	0.035391	-1.108028	1.219294	0.131281
2000-01-28	0.477427	-1.605746	-1.041161	-1.128379
2000-01-31	-0.955470	-0.491113	-0.738858	0.276953
2000-02-01	0.259385	0.335096	-0.485912	-0.007712
2000-02-02	-0.237599	-1.375179	-1.198458	-0.662935
2000-02-03	-0.598414	-0.261143	0.173009	-0.541031
2000-02-04	-1.290128	-1.422936	-2.372973	0.367791
2000-02-07	-0.716938	-1.103327	-0.906131	0.756418
2000-02-08	0.092658	0.755122	0.156270	-0.231200
2000-02-09	-0.860367	0.696204	0.346518	-0.347244
2000-02-10	-0.819556	-0.249229	0.136163	0.666071
2000-02-11	-0.133716	1.038838	-2.515921	1.147261

numpy¶

generating Array¶

Array indexing and slicing¶

Array concatenation¶

Array reshaping¶

Vectorized manipulations¶

Calculations with numpy¶

Masked arrays¶

pandas¶

Create a data frame¶

Quick production of dataframe examples with `pandas._testing`¶

Explorations on DatetimeIndex, PeriodIndex¶

read data¶

Quick summary¶

object vs. str¶

Sort, duplicates¶

Categorizing values¶

datetime type, time series as index¶

apply, groupby apply¶

multiindex (panel data)¶

merge¶

Reshaping and pivoting¶

Plotting¶

Matplotlib basics¶

an Seaborn example with masked data¶

Draw from pandas¶

	name	value
0	a	0
1	b	1
2	c	2
3	d	3
4	e	4
5	f	5
6	g	6
7	h	7
8	i	8
9	j	9
10	k	10
11	l	11
12	m	12
13	n	13
14	o	14
15	p	15
16	q	16
17	r	17
18	s	18
19	t	19
20	u	20
21	v	21
22	w	22
23	x	23
24	y	24
25	z	25

	A	B	C	D
0	0.0	0.0	foo1	2009-01-01
1	1.0	1.0	foo2	2009-01-02
2	2.0	0.0	foo3	2009-01-05
3	3.0	1.0	foo4	2009-01-06
4	4.0	0.0	foo5	2009-01-07

	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

	ticker	secShortName	tradeDate	preCloseIndex	openIndex	highestIndex	lowestIndex	closeIndex	turnoverVol
0	000001	上证综指	2021-01-04	3473.0693	3474.6793	3511.6554	3457.2061	3502.9584	38079080000
1	000001	上证综指	2021-01-05	3502.9584	3492.1912	3528.6767	3484.7151	3528.6767	40799593400
2	000001	上证综指	2021-01-06	3528.6767	3530.9072	3556.8022	3513.1262	3550.8767	37023092600
3	000001	上证综指	2021-01-07	3550.8767	3552.9087	3576.2046	3526.6174	3576.2046	40534822600
4	000001	上证综指	2021-01-08	3576.2046	3577.6923	3588.0625	3544.8912	3570.1082	34555789600
...	...	...	...	...	...	...	...	...	...
1360	399106	深证综指	2022-02-15	2253.1310	2256.8670	2283.6760	2251.3000	2283.6300	37139160803
1361	399106	深证综指	2022-02-16	2283.6300	2293.0800	2304.2800	2285.6750	2296.9930	37530877510
1362	399106	深证综指	2022-02-17	2296.9930	2293.4480	2314.8270	2287.8960	2302.1620	42928561256
1363	399106	深证综指	2022-02-18	2302.1620	2287.2640	2311.8220	2284.1220	2311.7900	38837049683
1364	399106	深证综指	2022-02-21	2311.7900	2313.5060	2325.9670	2309.7340	2325.7960	44801214227

	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
count	5.838750e+06	5.838750e+06	5.838750e+06	5.838750e+06	5.820845e+06	5.838750e+06	5.838750e+06	5.838750e+06
mean	1.368568e+02	1.368763e+02	5.525219e+06	2.008992e+08	1.078089e+04	2.986831e-02	1.324705e+10	1.835989e+10
std	2.086103e+03	2.085805e+03	2.405725e+07	4.866879e+08	1.874182e+04	4.649860e-02	5.686519e+10	7.522905e+10
min	3.800000e-02	3.800000e-02	2.000000e+00	1.800000e+01	1.000000e+00	0.000000e+00	3.800000e+06	1.045000e+07
25%	2.167700e+01	2.168600e+01	8.454962e+05	2.916335e+07	2.565000e+03	6.900000e-03	2.203200e+09	3.527868e+09
50%	3.972300e+01	3.973600e+01	1.864330e+06	7.414118e+07	5.410000e+03	1.510000e-02	4.304763e+09	6.153206e+09
75%	7.390000e+01	7.392400e+01	4.317285e+06	1.915373e+08	1.179500e+04	3.380000e-02	9.130280e+09	1.286893e+10
max	4.024782e+05	4.024782e+05	5.109897e+09	6.794128e+10	3.274456e+06	9.896000e-01	3.267370e+12	3.267370e+12

	A	B	C	D
2000-01-03	-0.095473	1.490130	0.790182	0.184909
2000-01-04	-0.248742	0.393997	1.307647	0.051067
2000-01-05	-1.235977	1.187502	-1.665897	-0.355819

	score
ocSZYYUkHs	53
gTKl4LEzN5	55
E3WFBpPnP1	64
l7t7hYzIjN	11
5MyJDWBGFj	98
V9PGZpnzu1	25
HHopAhwz1P	20
H8aIGxvWJP	33
yAE3gfP5fn	56
D3SfsS2ddm	15
EjwMd3huy1	97
SEsr3gTI6C	84
StlWk4TtWt	22
lJ99Pqv1zg	49
Eeoc8MbIok	91
pkJofTFlsG	89
kpbtUkTTTy	72
6K6nnrZZAz	0
pZyED2L9lP	7
5CF9jssPwu	42
5jiQoqwnOp	86
wFwwHYb4x0	22
humbKxNMFR	73
oC1e0WXbvX	12
PumV2ro6sp	20
KZu0LmKlTN	16
2JPkKXlSWj	99
JEcnqKLoo0	48
BDMmStdXla	91
jeoVnXIIgq	11

	secID	secShortName	exchangeCD	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
tradeDate
2015-01-05	000001.XSHE	平安银行	XSHE	1293.044	1307.737	4966040	4.565388e+09	92478.0	0.0291	1.575841e+11	1.830268e+11
2015-01-06	000001.XSHE	平安银行	XSHE	1307.737	1288.146	3761152	3.453446e+09	80325.0	0.0220	1.552233e+11	1.802848e+11
2015-01-07	000001.XSHE	平安银行	XSHE	1288.146	1263.656	2951601	2.634796e+09	72697.0	0.0173	1.522723e+11	1.768574e+11
2015-01-08	000001.XSHE	平安银行	XSHE	1263.656	1221.208	2443951	2.128003e+09	68734.0	0.0143	1.471572e+11	1.709164e+11
2015-01-09	000001.XSHE	平安银行	XSHE	1221.208	1231.004	4355039	3.835378e+09	99882.0	0.0255	1.483376e+11	1.722874e+11
...	...	...	...	...	...	...	...	...	...	...	...
2021-12-27	000001.XSHE	平安银行	XSHE	2224.866	2213.298	881462	1.260455e+09	66911.0	0.0038	3.341671e+11	3.341699e+11
2021-12-28	000001.XSHE	平安银行	XSHE	2213.298	2206.872	1358314	1.934461e+09	89671.0	0.0058	3.331968e+11	3.331996e+11
2021-12-29	000001.XSHE	平安银行	XSHE	2206.872	2152.889	1771527	2.480535e+09	147243.0	0.0076	3.250464e+11	3.250491e+11
2021-12-30	000001.XSHE	平安银行	XSHE	2152.889	2161.886	960485	1.342374e+09	66792.0	0.0041	3.264048e+11	3.264075e+11
2021-12-31	000001.XSHE	平安银行	XSHE	2161.886	2118.185	2110777	2.899617e+09	152423.0	0.0090	3.198068e+11	3.198095e+11

	secID	secShortName	exchangeCD	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
tradeDate
2015-01-30	000001.XSHE	平安银行	XSHE	1134.678	1137.127	1614788	1.298736e+09	42061.0	0.0095	1.370254e+11	1.591488e+11
2015-02-27	000001.XSHE	平安银行	XSHE	1148.556	1142.025	1377727	1.115146e+09	36250.0	0.0081	1.376156e+11	1.598343e+11
2015-03-31	000001.XSHE	平安银行	XSHE	1278.350	1285.697	4745114	4.393135e+09	114032.0	0.0278	1.549282e+11	1.799421e+11
2015-04-30	000001.XSHE	平安银行	XSHE	1645.458	1650.399	4182058	4.899380e+09	120845.0	0.0245	1.971277e+11	2.289549e+11
2015-05-29	000001.XSHE	平安银行	XSHE	1531.808	1514.019	2915126	3.103544e+09	99260.0	0.0171	1.808381e+11	2.192089e+11
...	...	...	...	...	...	...	...	...	...	...	...
2021-08-31	000001.XSHE	平安银行	XSHE	2277.563	2287.846	1416461	2.072007e+09	119791.0	0.0061	3.454224e+11	3.454253e+11
2021-09-30	000001.XSHE	平安银行	XSHE	2332.832	2304.555	959216	1.424676e+09	70140.0	0.0041	3.479452e+11	3.479481e+11
2021-10-29	000001.XSHE	平安银行	XSHE	2534.625	2506.348	650958	1.052760e+09	76331.0	0.0028	3.784122e+11	3.784154e+11
2021-11-30	000001.XSHE	平安银行	XSHE	2250.572	2241.575	884472	1.280385e+09	65260.0	0.0038	3.384364e+11	3.384392e+11
2021-12-31	000001.XSHE	平安银行	XSHE	2161.886	2118.185	2110777	2.899617e+09	152423.0	0.0090	3.198068e+11	3.198095e+11

	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
tradeDate
2015-01-31	1204.922300	1197.126450	2.685951e+06	2.293232e+09	64189.250000	0.015725	1.442554e+11	1.675461e+11
2015-02-28	1127.222600	1127.549133	1.506542e+06	1.202904e+09	37079.066667	0.008827	1.358712e+11	1.578083e+11
2015-03-31	1193.861227	1200.391773	2.966580e+06	2.552488e+09	75256.318182	0.017386	1.446489e+11	1.680031e+11
2015-04-30	1543.791762	1561.158524	4.287529e+06	4.712147e+09	138826.190476	0.025114	1.869631e+11	2.171492e+11
2015-05-31	1577.762100	1570.943100	2.897439e+06	3.198760e+09	96879.850000	0.016965	1.876373e+11	2.213028e+11
...	...	...	...	...	...	...	...	...
2021-08-31	2440.388364	2441.031045	1.143970e+06	1.800585e+09	96686.318182	0.004891	3.685506e+11	3.685537e+11
2021-09-30	2390.798950	2391.634400	1.659547e+06	2.550127e+09	126351.950000	0.007095	3.610926e+11	3.610956e+11
2021-10-31	2504.098750	2516.710812	1.081450e+06	1.752654e+09	97972.312500	0.004625	3.799768e+11	3.799800e+11
2021-11-30	2318.985409	2306.950273	1.188170e+06	1.768122e+09	94558.681818	0.005073	3.483068e+11	3.483098e+11
2021-12-31	2270.019130	2264.654348	1.342157e+06	1.968544e+09	96840.304348	0.005735	3.419210e+11	3.419238e+11

	Name	Age
a	tom	10
b	nick	15
c	juli	14

	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
0	1.671966e+06	1.710179e+06	24661553281603	2.084277e+19	8.552180e+09	3.000847	2.483276e+22	3.349881e+22
1	1.710179e+06	1.659323e+06	14146264367107	1.192629e+19	6.452106e+09	3.000484	2.409428e+22	3.250262e+22
2	1.659323e+06	1.596829e+06	8711948463204	6.942152e+18	5.284854e+09	3.000299	2.318685e+22	3.127853e+22
3	1.596829e+06	1.491352e+06	5972896490404	4.528399e+18	4.724363e+09	3.000204	2.165525e+22	2.921242e+22
4	1.491352e+06	1.515374e+06	18966364691524	1.471013e+19	9.976414e+09	3.000650	2.200405e+22	2.968295e+22
...	...	...	...	...	...	...	...	...
5838745	3.390625e+00	3.407044e+00	238642997124	9.495889e+10	5.107900e+04	3.000007	1.356573e+16	4.880432e+16
5838746	3.407044e+00	3.405769e+00	31578000807	1.251736e+10	4.099000e+03	3.000001	1.352290e+16	4.865024e+16
5838747	3.405769e+00	3.396900e+00	15264602503	5.980393e+09	3.367000e+03	3.000000	1.322500e+16	4.757852e+16
5838748	3.396900e+00	3.403225e+00	12904960003	5.084831e+09	1.684000e+03	3.000000	1.343745e+16	4.834282e+16
5838749	3.403225e+00	3.404496e+00	28156840003	1.123515e+10	5.479000e+03	3.000001	1.348014e+16	4.849641e+16

	secID	exchangeCD	tradeDate	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
secShortName
*ST万方	000638.XSHE	XSHE	2021-12-31	21.428	21.560	550679	1.185508e+07	794.0	0.0079	1.509872e+09	1.509872e+09
*ST上普	600680.XSHG	XSHG	2018-04-27	28.879	30.133	1272828	3.498398e+07	2843.0	0.0180	1.979601e+09	2.939313e+09
*ST东海A	000613.XSHE	XSHE	2021-12-31	5.678	5.836	3854756	2.219116e+07	1384.0	0.0207	1.088870e+09	1.485528e+09
*ST东海B	200613.XSHE	XSHE	2021-12-31	1.757	1.757	33909	6.009500e+04	13.0	0.0004	1.408000e+08	5.825600e+08
*ST东电	000585.XSHE	XSHE	2021-12-31	2.617	2.617	5993090	1.543082e+07	1268.0	0.0123	1.249313e+09	1.790408e+09
...	...	...	...	...	...	...	...	...	...	...	...
龙竹科技	831445.XBEI	XBEI	2021-12-31	20.824	21.098	456508	8.807597e+06	NaN	0.0068	1.297350e+09	2.217318e+09
龙腾光电	688055.XSHG	XSHG	2021-12-31	6.808	6.858	4664701	3.186770e+07	3830.0	0.0026	1.223867e+10	2.283333e+10
龙蟠科技	603906.XSHG	XSHG	2021-12-31	77.083	77.208	3700591	2.766969e+08	20421.0	0.0155	1.785667e+10	1.785667e+10
龙软科技	688078.XSHG	XSHG	2021-12-31	62.467	59.057	963837	5.722752e+07	2401.0	0.0339	1.657041e+09	4.117650e+09
龙高股份	605086.XSHG	XSHG	2021-12-31	23.830	23.620	833004	1.976806e+07	1403.0	0.0260	7.558400e+08	3.023360e+09

	secID	exchangeCD	tradeDate	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
secShortName
*ST万方	000638.XSHE	XSHE	2015-01-05	28.364	27.304	3103725	8.408389e+07	4279.0	0.0443	1.912092e+09	1.912092e+09
*ST上普	600680.XSHG	XSHG	2015-01-05	57.366	57.562	483414	2.566049e+07	1787.0	0.0069	3.781578e+09	5.614890e+09
*ST东海A	000613.XSHE	XSHE	2015-01-05	9.769	9.783	2242078	2.191941e+07	1159.0	0.0123	1.782263e+09	2.490444e+09
*ST东海B	200613.XSHE	XSHE	2015-01-05	6.642	6.576	164636	1.085171e+06	50.0	0.0021	5.271200e+08	2.180959e+09
*ST东电	000585.XSHE	XSHE	2015-01-05	5.183	5.272	9358955	4.800403e+07	4519.0	0.0192	2.516909e+09	3.607018e+09
...	...	...	...	...	...	...	...	...	...	...	...
龙竹科技	831445.XBEI	XBEI	2020-07-27	9.180	7.840	4804739	4.053933e+07	NaN	0.0730	5.157141e+08	8.976079e+08
龙腾光电	688055.XSHG	XSHG	2020-08-17	1.220	9.850	215585877	1.892487e+09	211520.0	0.8003	2.653273e+09	3.283333e+10
龙蟠科技	603906.XSHG	XSHG	2017-04-10	9.520	13.710	11500	1.576650e+05	15.0	0.0002	7.129200e+08	2.851680e+09
龙软科技	688078.XSHG	XSHG	2019-12-30	21.590	48.900	12454266	6.069590e+08	28417.0	0.7736	7.872460e+08	3.459675e+09
龙高股份	605086.XSHG	XSHG	2021-04-16	12.860	18.520	226150	4.186135e+06	345.0	0.0071	5.926400e+08	2.370560e+09

	name	value
0	a	0
1	b	1
2	c	2
3	d	3
4	e	4
5	f	5
6	g	6
7	h	7
8	i	8
9	j	9
10	k	10
11	l	11
12	m	12
13	n	13
14	o	14
15	p	15
16	q	16
17	r	17
18	s	18
19	t	19
20	u	20
21	v	21
22	w	22
23	x	23
24	y	24
25	z	25

		0	1	2	3
bar	1	-0.006910	-0.495349	1.583263	0.679016
	2	-0.264934	0.034949	1.936711	0.711140
	3	-1.161140	-0.364241	0.874213	-0.482764
baz	1	-1.672136	0.990049	0.068838	-0.474269
	2	0.007340	0.476043	0.561914	-0.520332
	3	-1.266026	0.144909	2.426870	-0.361181
foo	1	0.272596	-1.057101	1.400982	1.160019
	2	-0.912277	0.609319	-0.361195	-0.668084
	3	0.214207	-0.023626	1.501661	-0.926852
qux	1	-2.123011	0.508700	-1.222445	-0.634993
	2	0.826994	1.076484	0.884695	-0.682403
	3	0.015557	-0.419355	-1.725047	0.314541

		secID	exchangeCD	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
secShortName	tradeDate
宁德时代	2018-06-11	300750.XSHE	XSHE	25.140	36.200	78829	2.845472e+06	154.0	0.0004	7.864222e+09	7.864222e+10
	2018-06-12	300750.XSHE	XSHE	36.200	39.820	26579	1.058376e+06	86.0	0.0001	8.650644e+09	8.650644e+10
	2018-06-13	300750.XSHE	XSHE	39.820	43.800	45030	1.972314e+06	98.0	0.0002	9.515274e+09	9.515274e+10
	2018-06-14	300750.XSHE	XSHE	43.800	48.180	74267	3.578184e+06	211.0	0.0003	1.046680e+10	1.046680e+11
	2018-06-15	300750.XSHE	XSHE	48.180	53.000	256510	1.359503e+07	717.0	0.0012	1.151392e+10	1.151392e+11
...	...	...	...	...	...	...	...	...	...	...	...
平安银行	2020-01-20	000001.XSHE	XSHE	2055.130	2062.654	899493	1.226465e+09	42805.0	0.0038	3.192246e+11	3.192274e+11
	2020-01-21	000001.XSHE	XSHE	2062.654	2006.229	1080975	1.442171e+09	60130.0	0.0046	3.104920e+11	3.104947e+11
	2020-01-22	000001.XSHE	XSHE	2006.229	2017.514	867411	1.150933e+09	49245.0	0.0037	3.122386e+11	3.122412e+11
	2020-01-23	000001.XSHE	XSHE	2017.514	1948.549	1326911	1.723394e+09	71683.0	0.0057	3.015654e+11	3.015680e+11
	2020-02-03	000001.XSHE	XSHE	1948.549	1754.196	2723762	3.201454e+09	105935.0	0.0116	2.714865e+11	2.714888e+11

	A	B	C	D
2000-01-03	1.314670	-1.439479	-0.400497	-0.759633
2000-01-04	-1.281146	0.317622	0.562698	1.176647
2000-01-05	-0.627046	0.507325	0.230216	1.652183

		value
date	variable
2000-01-03	A	1.314670
2000-01-04	A	-1.281146
2000-01-05	A	-0.627046
2000-01-03	B	-1.439479
2000-01-04	B	0.317622
2000-01-05	B	0.507325
2000-01-03	C	-0.400497
2000-01-04	C	0.562698
2000-01-05	C	0.230216
2000-01-03	D	-0.759633
2000-01-04	D	1.176647
2000-01-05	D	1.652183

	date	variable	value	value2
0	2000-01-03	A	1.314670	1.728358
1	2000-01-04	A	-1.281146	1.641335
2	2000-01-05	A	-0.627046	0.393187
3	2000-01-03	B	-1.439479	2.072100
4	2000-01-04	B	0.317622	0.100884
5	2000-01-05	B	0.507325	0.257379
6	2000-01-03	C	-0.400497	0.160398
7	2000-01-04	C	0.562698	0.316629
8	2000-01-05	C	0.230216	0.053000
9	2000-01-03	D	-0.759633	0.577043
10	2000-01-04	D	1.176647	1.384498
11	2000-01-05	D	1.652183	2.729708

	preClosePrice	closePrice	turnoverVol	turnoverValue	dealAmount	turnoverRate	negMarketValue	marketValue
preClosePrice	1.000000	0.997575	0.064982	0.389881	0.601451	-0.039942	0.989830	0.994295
closePrice	0.997575	1.000000	0.075458	0.400851	0.606522	-0.030710	0.991942	0.996452
turnoverVol	0.064982	0.075458	1.000000	0.924077	0.752066	0.981827	0.027619	0.055961
turnoverValue	0.389881	0.400851	0.924077	1.000000	0.914040	0.862471	0.355060	0.383088
dealAmount	0.601451	0.606522	0.752066	0.914040	1.000000	0.665757	0.573258	0.596717
turnoverRate	-0.039942	-0.030710	0.981827	0.862471	0.665757	1.000000	-0.090386	-0.050985
negMarketValue	0.989830	0.991942	0.027619	0.355060	0.573258	-0.090386	1.000000	0.993615
marketValue	0.994295	0.996452	0.055961	0.383088	0.596717	-0.050985	0.993615	1.000000

numpy¶

generating Array¶

Array indexing and slicing¶

Array concatenation¶

Array reshaping¶

Vectorized manipulations¶

Calculations with numpy¶

Masked arrays¶

pandas¶

Create a data frame¶

Quick production of dataframe examples with pandas._testing¶

Explorations on DatetimeIndex, PeriodIndex¶

read data¶

Quick summary¶

object vs. str¶

Sort, duplicates¶

Categorizing values¶

datetime type, time series as index¶

apply, groupby apply¶

multiindex (panel data)¶

merge¶

Reshaping and pivoting¶

Plotting¶

Matplotlib basics¶

an Seaborn example with masked data¶

Draw from pandas¶

Quick production of dataframe examples with `pandas._testing`¶

	name	value
0	a	0
1	b	1
2	c	2
3	d	3
4	e	4
5	f	5
6	g	6
7	h	7
8	i	8
9	j	9
10	k	10
11	l	11
12	m	12
13	n	13
14	o	14
15	p	15
16	q	16
17	r	17
18	s	18
19	t	19
20	u	20
21	v	21
22	w	22
23	x	23
24	y	24
25	z	25