​一、用法精讲

# 68、pandas.infer_freq函数
pandas.infer_freq(index)
Infer the most likely frequency given the input index.

Parameters:
index
DatetimeIndex, TimedeltaIndex, Series or array-like
If passed a Series will use the values of the series (NOT THE INDEX).

Returns:
str or None
None if no discernible frequency.

Raises:
TypeError
If the index is not datetime-like.

ValueError
If there are fewer than three values.

无

# 68、pandas.infer_freq函数
# 68-1、使用DatetimeIndex推断频率
import pandas as pd
# 创建一个DatetimeIndex
dates = pd.date_range(start='2023-01-01', periods=10, freq='D')
# 推断频率
freq = pd.infer_freq(dates)
print(f"推断的频率是: {freq}", end='\n\n')

# 68-2、推断非标准频率
import pandas as pd
# 创建一个DatetimeIndex，但这次我们使用非标准的天数间隔
dates = pd.to_datetime(['2024-01-01', '2024-01-03', '2024-01-06'])
# 尝试推断频率
freq = pd.infer_freq(dates)
print(f"推断的频率是: {freq}", end='\n\n')

# 68-3、使用TimedeltaIndex推断频率
import pandas as pd
# 创建一个TimedeltaIndex
timedeltas = pd.to_timedelta(['1 days', '2 days', '3 days'])
# 尝试推断频率
freq = pd.infer_freq(timedeltas)
print(f"推断的频率是: {freq}")

# 68、pandas.infer_freq函数
# 68-1、使用DatetimeIndex推断频率
# 推断的频率是: D

# 68-2、推断非标准频率
# 推断的频率是: None

# 68-3、使用TimedeltaIndex推断频率
# 推断的频率是: D

# 69、pandas.interval_range函数
pandas.interval_range(start=None, end=None, periods=None, freq=None, name=None, closed='right')
Return a fixed frequency IntervalIndex.

Parameters:
start
numeric or datetime-like, default None
Left bound for generating intervals.

end
numeric or datetime-like, default None
Right bound for generating intervals.

periods
int, default None
Number of periods to generate.

freq
numeric, str, Timedelta, datetime.timedelta, or DateOffset, default None
The length of each interval. Must be consistent with the type of start and end, e.g. 2 for numeric, or ‘5H’ for datetime-like. Default is 1 for numeric and ‘D’ for datetime-like.

name
str, default None
Name of the resulting IntervalIndex.

closed
{‘left’, ‘right’, ‘both’, ‘neither’}, default ‘right’
Whether the intervals are closed on the left-side, right-side, both or neither.

Returns:
IntervalIndex

无

# 70、pandas.eval函数
pandas.eval(expr, parser='pandas', engine=None, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False)
Evaluate a Python expression as a string using various backends.

The following arithmetic operations are supported: +, -, *, /, **, %, // (python engine only) along with the following boolean operations: | (or), & (and), and ~ (not). Additionally, the 'pandas' parser allows the use of and, or, and not with the same semantics as the corresponding bitwise operators. Series and DataFrame objects are supported and behave as they would with plain ol’ Python evaluation.

Parameters:
exprstr
The expression to evaluate. This string cannot contain any Python statements, only Python expressions.

parser{‘pandas’, ‘python’}, default ‘pandas’
The parser to use to construct the syntax tree from the expression. The default of 'pandas' parses code slightly different than standard Python. Alternatively, you can parse an expression using the 'python' parser to retain strict Python semantics. See the enhancing performance documentation for more details.

engine{‘python’, ‘numexpr’}, default ‘numexpr’
The engine used to evaluate the expression. Supported engines are

None : tries to use numexpr, falls back to python

'numexpr' : This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames.

'python' : Performs operations as if you had eval’d in top level python. This engine is generally not that useful.

More backends may be available in the future.

local_dictdict or None, optional
A dictionary of local variables, taken from locals() by default.

global_dictdict or None, optional
A dictionary of global variables, taken from globals() by default.

resolverslist of dict-like or None, optional
A list of objects implementing the __getitem__ special method that you can use to inject an additional collection of namespaces to use for variable lookup. For example, this is used in the query() method to inject the DataFrame.index and DataFrame.columns variables that refer to their respective DataFrame instance attributes.

levelint, optional
The number of prior stack frames to traverse and add to the current scope. Most users will not need to change this parameter.

targetobject, optional, default None
This is the target object for assignment. It is used when there is variable assignment in the expression. If so, then target must support item assignment with string keys, and if a copy is being returned, it must also support .copy().

inplacebool, default False
If target is provided, and the expression mutates target, whether to modify target inplace. Otherwise, return a copy of target with the mutation.

Returns:
ndarray, numeric scalar, DataFrame, Series, or None
The completion value of evaluating the given code or None if inplace=True.

Raises:
ValueError
There are many instances where such an error can be raised:

target=None, but the expression is multiline.

The expression is multiline, but not all them have item assignment. An example of such an arrangement is this:

a = b + 1 a + 2

Here, there are expressions on different lines, making it multiline, but the last line has no variable assigned to the output of a + 2.

inplace=True, but the expression is missing item assignment.

Item assignment is provided, but the target does not support string item assignment.

Item assignment is provided and inplace=False, but the target does not support the .copy() method

无

# 70、pandas.eval函数
# 70-1、返回表达式的计算结果
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
result = pd.eval('A + B', local_dict={'A': df['A'], 'B': df['B']})
print(result, end='\n\n')

# 70-2、直接在DataFrame上使用
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df.eval('C = A + B', inplace=True)
print(df)

# 70、pandas.eval函数
# 70-1、返回表达式的计算结果
# 0    5
# 1    7
# 2    9
# dtype: int64

# 70-2、直接在DataFrame上使用
#    A  B  C
# 0  1  4  5
# 1  2  5  7
# 2  3  6  9