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| ====Series数据操作==== | | ====Series数据操作==== |
|
| |
|
| ====Series属性==== | | ===DataFrame=== |
| 下表示例中s为Series对象:
| | DataFrame是有标记的二维的数据结构,具有可能不同类型的列。由数据,行标签(索引,index),列标签(列,columns)构成。您可以将其视为电子表格或SQL表,或Series对象的字典。它通常是最常用的Pandas对象。 |
| | |
| | {{了解更多|[https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe Pandas 用户指南:DataFrame]}} |
| | ====创建DataFrame==== |
| | 创建DataFrame对象有多种方法: |
| | * 使用<code>pandas.DataFrame()</code>构造方法 |
| | * 使用<code>pandas.DataFrame.from_dict()</code>方法,类似构造方法 |
| | * 使用<code>pandas.DataFrame.from_records()</code>方法,类似构造方法 |
| | * 使用函数从导入文件创建,如使用<code>pandas.read_csv()</code>函数导入csv文件创建一个DataFrame对象。 |
| | |
| | 构造方法<code>pandas.DataFrame()</code>的格式为: |
| | pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False) |
| | |
| | |
| | ===属性和方法=== |
| | 下面将Series和DataFrame的属性、方法按作用分类展示。 |
| | |
| | 表示例中s为一个Series对象,df为一个DataFrame对象: |
| <syntaxhighlight lang="python" > | | <syntaxhighlight lang="python" > |
| >>> s = pd.Series(['a', 'b', 'c']) | | >>> s = pd.Series(['a', 'b', 'c']) |
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| 2 c | | 2 c |
| dtype: object | | dtype: object |
| | |
| </syntaxhighlight> | | </syntaxhighlight> |
| | |
| | {{了解更多 |
| | |[https://pandas.pydata.org/docs/reference/frame.html Pandas API:DataFrame] |
| | |[https://pandas.pydata.org/docs/reference/series.html Pandas API:Series]}} |
| | ====构造方法==== |
| {| class="wikitable" | | {| class="wikitable" |
| |- | | |- |
| !属性名 | | !方法名 |
| !描述 | | !描述 |
| | !Series |
| | !DataFrame |
| !示例 | | !示例 |
| !结果
| |
| |- | | |- |
| | T | | |构造方法 |
| | 返回转置,根据定义,Series转置为自身。 | | |创建一个Series对象或DataFrame对象 |
| | s.T | | |pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False) |
| | 自身 | | |pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False) |
| | |<code>s = pd.Series(["a", "b", "c"])</code> <br \><br \><code>df = pd.DataFrame([['foo', 22, 3], ['bar', 25, 6], ['test', 18, 7]],columns=['name', 'age', 'number'])</code> |
| | |- |
| | |} |
| | |
| | ====属性和基本信息==== |
| | {| class="wikitable" |
| | |- |
| | !属性/方法 |
| | !描述 |
| | !Series |
| | !DataFrame |
| | !示例 |
| | |- |
| | | index |
| | | 索引(行标签) |
| | |Series.index |
| | |DataFrame.index |
| | | <code>s.index</code> <br \> <code>df.index</code> |
| | |- |
| | | columns |
| | | 列标签,Series无 |
| | | − |
| | |DataFrame.columns |
| | | <code>df.columns</code> |
| | |- |
| | | dtypes |
| | | 返回数据的Numpy数据类型(dtype对象) |
| | |Series.index |
| | |DataFrame.index |
| | | <code>s.dtypes</code><br \> <code>df.dtypes</code> |
| | |- |
| | | dtype |
| | | 返回数据的Numpy数据类型(dtype对象) |
| | | Series.index |
| | | − |
| | | <code>s.dtype</code> |
| |- | | |- |
| | array | | | array |
| | 返回 Series 或 Index 数据的数组,该数组为pangdas扩展的python数组. | | | 返回 Series 或 Index 数据的数组,该数组为pangdas扩展的python数组. |
| | s.array | | | Series.index |
| | <PandasArray><br \>['a', 'b', 'c']<br \>Length: 3, dtype: object
| | | − |
| |-
| | | <code>s.array</code> <br \>返回:<PandasArray><br \>['a', 'b', 'c']<br \>Length: 3, dtype: object |
| | at
| |
| | 通过行轴和列轴标签获取或设置单个值。
| |
| | s.at[1]<br \>s.at[2]='d'
| |
| |'b'
| |
| |- | | |- |
| | attrs | | | attrs |
| | 此对象全局属性字典。 | | | 此对象全局属性字典。 |
| | s.attrs | | | Series.attrs |
| | {} | | | DataFrame.attrs |
| | | <code>s.attrs</code>返回{} |
| |- | | |- |
| | axes | | | axes |
| | 返回行轴标签的列表。 | | | 返回轴标签的列表。<br \>Series返回[index] <br \>DataFrame返回[index, columns] |
| | s.axes | | | Series.axes |
| | [RangeIndex(start=0, stop=3, step=1)] | | | DataFrame.axes |
| | | <code>s.axes</code>返回[RangeIndex(start=0, stop=3, step=1)] |
| |- | | |- |
| | dtype | | | hasnans |
| | 返回数据的Numpy数据类型 | | | 如果有任何空值(如Python的None,np.NaN)返回True,否则返回False。 |
| | s.dtype | | | Series.hasnans |
| | dtype('O') | | | − |
| | | <code>s2 = pd.Series(['a', None, 'c'])</code> <br \><code>s2.hasnans</code> <br \>返回True |
| | |- |
| | |} |
| | |
| | ====数据选取/索引标签/迭代==== |
| | {| class="wikitable" |
| |- | | |- |
| | dtypes
| | !属性/方法 |
| | 返回数据的Numpy数据类型
| | !描述 |
| | s.dtypes
| | !Series |
| | dtype('O')
| | !DataFrame |
| | !示例 |
| |- | | |- |
| | hasnans | | | at |
| | 如果有任何空值(如Python的None,np.NaN)返回True,否则返回False。 | | | 通过行轴和列轴标签对获取或设置单个值。 |
| | s2 = pd.Series(['a', None, 'c']) <br \>s2.hasnans | | | Series.at |
| | True
| | | DataFrame.at |
| | | <code>s.at[1]</code>返回'b'<br \><code>s.at[2]='d'</code>设置索引位置为第三的值等于'd' |
| |- | | |- |
| | iat | | | iat |
| | 通过行轴和列轴整数位置获取或设置单个值。 | | | 通过行轴和列轴整数位置获取或设置单个值。 |
| | s.iat[1]<br \>s.iat[2]='d' | | | Series.iat |
| |'b'
| | | DataFrame.iat |
| | | <code>s.iat[1]</code><br \><code>s.iat[2]='d'</code> |
| |- | | |- |
| | iloc | | | iloc |
| |通过索引(行轴)整数位置获取或设置值。 | | |通过索引(行轴)整数位置获取或设置值。 |
| |1. <code>s.iloc[2]</code> <br \>2. <code>s.iloc[:2]</code> <br \>3. <code><nowiki>s.iloc[[True,False,True]]</nowiki></code> <br \>4. <code>s.iloc[lambda x: x.index % 2 == 0]</code> | | | Series.iloc |
| |1. 'b'<br \>2. 选取索引为0到2(不包含2)的值<br \>3. 选取索引位置为True的值 <br \>4. 选取索引为双数的值
| | | DataFrame.iloc |
| |-
| | |<code>s.iloc[2]</code>结果为'b' <br \><code>s.iloc[:2]</code> 选取索引为0到2(不包含2)的值 <br \><code><nowiki>s.iloc[[True,False,True]]</nowiki></code>选取索引位置为True的值 <br \><code>s.iloc[lambda x: x.index % 2 == 0]</code>选取索引为双数的值 |
| | index
| |
| | The index (axis labels) of the Series.
| |
| |-
| |
| | is_monotonic
| |
| | Return boolean if values in the object are monotonic_increasing.
| |
| |-
| |
| | is_monotonic_decreasing
| |
| | Return boolean if values in the object are monotonic_decreasing.
| |
| |-
| |
| | is_monotonic_increasing
| |
| | Alias for is_monotonic.
| |
| |-
| |
| | is_unique
| |
| | Return boolean if values in the object are unique.
| |
| |-
| |
| | loc
| |
| | Access a group of rows and columns by label(s) or a boolean array.
| |
| |-
| |
| | name
| |
| | Return the name of the Series.
| |
| |-
| |
| | nbytes
| |
| | Return the number of bytes in the underlying data.
| |
| |-
| |
| | ndim
| |
| | Number of dimensions of the underlying data, by definition 1.
| |
| |-
| |
| | shape
| |
| | Return a tuple of the shape of the underlying data.
| |
| |-
| |
| | size
| |
| | Return the number of elements in the underlying data.
| |
| |- | | |- |
| | values
| |
| | Return Series as ndarray or ndarray-like depending on the dtype.
| |
| |} | | |} |
| {{了解更多|[https://pandas.pydata.org/docs/reference/api/pandas.Series.html#pandas.Series Pandas API:pandas.Series]}}
| | ====计算/描述统计==== |
| ====Series方法==== | |
| {| class="wikitable" | | {| class="wikitable" |
| |- | | |- |
| ! 方法 | | !属性/方法 |
| ! 描述 | | !描述 |
| ! 示例 | | !Series |
| ! 结果 | | !DataFrame |
| | !示例 |
| |- | | |- |
| | abs() | | | abs() |
| | 返回 Series/DataFrame 每个元素的绝对值。 | | | 返回 Series/DataFrame 每个元素的绝对值。 |
| | s.abs() | | | Series.abs() |
| | | | | DataFrame.abs() |
| |-
| | | <code>s.abs()</code> <br \> <code>df.abs()</code> |
| | add(other[, level, fill_value, axis])
| |
| | Return Addition of series and other, element-wise (binary operator add).
| |
| |
| |
| |
| |
| |-
| |
| | add_prefix(prefix)
| |
| | Prefix labels with string prefix. | |
| |-
| |
| | add_suffix(suffix)
| |
| | Suffix labels with string suffix.
| |
| |-
| |
| | agg([func, axis])
| |
| | Aggregate using one or more operations over the specified axis.
| |
| |- | | |- |
| | aggregate([func, axis])
| |
| | Aggregate using one or more operations over the specified axis.
| |
| |-
| |
| | align(other[, join, axis, level, copy, …])
| |
| | Align two objects on their axes with the specified join method.
| |
| |-
| |
| | all([axis, bool_only, skipna, level])
| |
| | Return whether all elements are True, potentially over an axis.
| |
| |-
| |
| | any([axis, bool_only, skipna, level])
| |
| | Return whether any element is True, potentially over an axis.
| |
| |-
| |
| | append(to_append[, ignore_index, …])
| |
| | Concatenate two or more Series.
| |
| |-
| |
| | apply(func[, convert_dtype, args])
| |
| | Invoke function on values of Series.
| |
| |-
| |
| | argmax([axis, skipna])
| |
| | Return int position of the largest value in the Series.
| |
| |-
| |
| | argmin([axis, skipna])
| |
| | Return int position of the smallest value in the Series.
| |
| |-
| |
| | argsort([axis, kind, order])
| |
| | Return the integer indices that would sort the Series values.
| |
| |-
| |
| | asfreq(freq[, method, how, normalize, …])
| |
| | Convert TimeSeries to specified frequency.
| |
| |-
| |
| | asof(where[, subset])
| |
| | Return the last row(s) without any NaNs before where.
| |
| |-
| |
| | astype(dtype[, copy, errors])
| |
| | Cast a pandas object to a specified dtype dtype.
| |
| |-
| |
| | at_time(time[, asof, axis])
| |
| | Select values at particular time of day (e.g., 9:30AM).
| |
| |-
| |
| | autocorr([lag])
| |
| | Compute the lag-N autocorrelation.
| |
| |-
| |
| | backfill([axis, inplace, limit, downcast])
| |
| | Synonym for DataFrame.fillna() with method='bfill'.
| |
| |-
| |
| | between(left, right[, inclusive])
| |
| | Return boolean Series equivalent to left <= series <= right.
| |
| |-
| |
| | between_time(start_time, end_time[, …])
| |
| | Select values between particular times of the day (e.g., 9:00-9:30 AM).
| |
| |-
| |
| | bfill([axis, inplace, limit, downcast])
| |
| | Synonym for DataFrame.fillna() with method='bfill'.
| |
| |-
| |
| | bool()
| |
| | Return the bool of a single element Series or DataFrame.
| |
| |-
| |
| | cat
| |
| | alias of pandas.core.arrays.categorical.CategoricalAccessor
| |
| |-
| |
| | clip([lower, upper, axis, inplace])
| |
| | Trim values at input threshold(s).
| |
| |-
| |
| | combine(other, func[, fill_value])
| |
| | Combine the Series with a Series or scalar according to func.
| |
| |-
| |
| | combine_first(other)
| |
| | Combine Series values, choosing the calling Series’s values first.
| |
| |-
| |
| | compare(other[, align_axis, keep_shape, …])
| |
| | Compare to another Series and show the differences.
| |
| |-
| |
| | convert_dtypes([infer_objects, …])
| |
| | Convert columns to best possible dtypes using dtypes supporting pd.NA.
| |
| |-
| |
| | copy([deep])
| |
| | Make a copy of this object’s indices and data.
| |
| |-
| |
| | corr(other[, method, min_periods])
| |
| | Compute correlation with other Series, excluding missing values.
| |
| |-
| |
| | count([level])
| |
| | Return number of non-NA/null observations in the Series.
| |
| |-
| |
| | cov(other[, min_periods, ddof])
| |
| | Compute covariance with Series, excluding missing values.
| |
| |-
| |
| | cummax([axis, skipna])
| |
| | Return cumulative maximum over a DataFrame or Series axis.
| |
| |-
| |
| | cummin([axis, skipna])
| |
| | Return cumulative minimum over a DataFrame or Series axis.
| |
| |-
| |
| | cumprod([axis, skipna])
| |
| | Return cumulative product over a DataFrame or Series axis.
| |
| |-
| |
| | cumsum([axis, skipna])
| |
| | Return cumulative sum over a DataFrame or Series axis.
| |
| |-
| |
| | describe([percentiles, include, exclude, …])
| |
| | Generate descriptive statistics.
| |
| |-
| |
| | diff([periods])
| |
| | First discrete difference of element.
| |
| |-
| |
| | div(other[, level, fill_value, axis])
| |
| | Return Floating division of series and other, element-wise (binary operator truediv).
| |
| |-
| |
| | divide(other[, level, fill_value, axis])
| |
| | Return Floating division of series and other, element-wise (binary operator truediv).
| |
| |-
| |
| | divmod(other[, level, fill_value, axis])
| |
| | Return Integer division and modulo of series and other, element-wise (binary operator divmod).
| |
| |-
| |
| | dot(other)
| |
| | Compute the dot product between the Series and the columns of other.
| |
| |-
| |
| | drop([labels, axis, index, columns, level, …])
| |
| | Return Series with specified index labels removed.
| |
| |-
| |
| | drop_duplicates([keep, inplace])
| |
| | Return Series with duplicate values removed.
| |
| |-
| |
| | droplevel(level[, axis])
| |
| | Return DataFrame with requested index / column level(s) removed.
| |
| |-
| |
| | dropna([axis, inplace, how])
| |
| | Return a new Series with missing values removed.
| |
| |-
| |
| | dt
| |
| | alias of pandas.core.indexes.accessors.CombinedDatetimelikeProperties
| |
| |-
| |
| | duplicated([keep])
| |
| | Indicate duplicate Series values.
| |
| |-
| |
| | eq(other[, level, fill_value, axis])
| |
| | Return Equal to of series and other, element-wise (binary operator eq).
| |
| |-
| |
| | equals(other)
| |
| | Test whether two objects contain the same elements.
| |
| |-
| |
| | ewm([com, span, halflife, alpha, …])
| |
| | Provide exponential weighted (EW) functions.
| |
| |-
| |
| | expanding([min_periods, center, axis])
| |
| | Provide expanding transformations.
| |
| |-
| |
| | explode([ignore_index])
| |
| | Transform each element of a list-like to a row.
| |
| |-
| |
| | factorize([sort, na_sentinel])
| |
| | Encode the object as an enumerated type or categorical variable.
| |
| |-
| |
| | ffill([axis, inplace, limit, downcast])
| |
| | Synonym for DataFrame.fillna() with method='ffill'.
| |
| |-
| |
| | fillna([value, method, axis, inplace, …])
| |
| | Fill NA/NaN values using the specified method.
| |
| |-
| |
| | filter([items, like, regex, axis])
| |
| | Subset the dataframe rows or columns according to the specified index labels.
| |
| |-
| |
| | first(offset)
| |
| | Select initial periods of time series data based on a date offset.
| |
| |-
| |
| | first_valid_index()
| |
| | Return index for first non-NA/null value.
| |
| |-
| |
| | floordiv(other[, level, fill_value, axis])
| |
| | Return Integer division of series and other, element-wise (binary operator floordiv).
| |
| |-
| |
| | ge(other[, level, fill_value, axis])
| |
| | Return Greater than or equal to of series and other, element-wise (binary operator ge).
| |
| |-
| |
| | get(key[, default])
| |
| | Get item from object for given key (ex: DataFrame column).
| |
| |-
| |
| | groupby([by, axis, level, as_index, sort, …])
| |
| | Group Series using a mapper or by a Series of columns.
| |
| |-
| |
| | gt(other[, level, fill_value, axis])
| |
| | Return Greater than of series and other, element-wise (binary operator gt).
| |
| |-
| |
| | head([n])
| |
| | Return the first n rows.
| |
| |-
| |
| | hist([by, ax, grid, xlabelsize, xrot, …])
| |
| | Draw histogram of the input series using matplotlib.
| |
| |-
| |
| | idxmax([axis, skipna])
| |
| | Return the row label of the maximum value.
| |
| |-
| |
| | idxmin([axis, skipna])
| |
| | Return the row label of the minimum value.
| |
| |-
| |
| | infer_objects()
| |
| | Attempt to infer better dtypes for object columns.
| |
| |-
| |
| | interpolate([method, axis, limit, inplace, …])
| |
| | Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex.
| |
| |-
| |
| | isin(values)
| |
| | Whether elements in Series are contained in values.
| |
| |-
| |
| | isna()
| |
| | Detect missing values.
| |
| |-
| |
| | isnull()
| |
| | Detect missing values.
| |
| |-
| |
| | item()
| |
| | Return the first element of the underlying data as a python scalar.
| |
| |-
| |
| | items()
| |
| | Lazily iterate over (index, value) tuples.
| |
| |-
| |
| | iteritems()
| |
| | Lazily iterate over (index, value) tuples.
| |
| |-
| |
| | keys()
| |
| | Return alias for index.
| |
| |-
| |
| | kurt([axis, skipna, level, numeric_only])
| |
| | Return unbiased kurtosis over requested axis.
| |
| |-
| |
| | kurtosis([axis, skipna, level, numeric_only])
| |
| | Return unbiased kurtosis over requested axis.
| |
| |-
| |
| | last(offset)
| |
| | Select final periods of time series data based on a date offset.
| |
| |-
| |
| | last_valid_index()
| |
| | Return index for last non-NA/null value.
| |
| |-
| |
| | le(other[, level, fill_value, axis])
| |
| | Return Less than or equal to of series and other, element-wise (binary operator le).
| |
| |-
| |
| | lt(other[, level, fill_value, axis])
| |
| | Return Less than of series and other, element-wise (binary operator lt).
| |
| |-
| |
| | mad([axis, skipna, level])
| |
| | Return the mean absolute deviation of the values for the requested axis.
| |
| |-
| |
| | map(arg[, na_action])
| |
| | Map values of Series according to input correspondence.
| |
| |-
| |
| | mask(cond[, other, inplace, axis, level, …])
| |
| | Replace values where the condition is True.
| |
| |-
| |
| | max([axis, skipna, level, numeric_only])
| |
| | Return the maximum of the values for the requested axis.
| |
| |-
| |
| | mean([axis, skipna, level, numeric_only])
| |
| | Return the mean of the values for the requested axis.
| |
| |-
| |
| | median([axis, skipna, level, numeric_only])
| |
| | Return the median of the values for the requested axis.
| |
| |-
| |
| | memory_usage([index, deep])
| |
| | Return the memory usage of the Series.
| |
| |-
| |
| | min([axis, skipna, level, numeric_only])
| |
| | Return the minimum of the values for the requested axis.
| |
| |-
| |
| | mod(other[, level, fill_value, axis])
| |
| | Return Modulo of series and other, element-wise (binary operator mod).
| |
| |-
| |
| | mode([dropna])
| |
| | Return the mode(s) of the dataset.
| |
| |-
| |
| | mul(other[, level, fill_value, axis])
| |
| | Return Multiplication of series and other, element-wise (binary operator mul).
| |
| |-
| |
| | multiply(other[, level, fill_value, axis])
| |
| | Return Multiplication of series and other, element-wise (binary operator mul).
| |
| |-
| |
| | ne(other[, level, fill_value, axis])
| |
| | Return Not equal to of series and other, element-wise (binary operator ne).
| |
| |-
| |
| | nlargest([n, keep])
| |
| | Return the largest n elements.
| |
| |-
| |
| | notna()
| |
| | Detect existing (non-missing) values.
| |
| |-
| |
| | notnull()
| |
| | Detect existing (non-missing) values.
| |
| |-
| |
| | nsmallest([n, keep])
| |
| | Return the smallest n elements.
| |
| |-
| |
| | nunique([dropna])
| |
| | Return number of unique elements in the object.
| |
| |-
| |
| | pad([axis, inplace, limit, downcast])
| |
| | Synonym for DataFrame.fillna() with method='ffill'.
| |
| |-
| |
| | pct_change([periods, fill_method, limit, freq])
| |
| | Percentage change between the current and a prior element.
| |
| |-
| |
| | pipe(func, *args, **kwargs)
| |
| | Apply func(self, *args, **kwargs).
| |
| |-
| |
| | plot
| |
| | alias of pandas.plotting._core.PlotAccessor
| |
| |-
| |
| | pop(item)
| |
| | Return item and drops from series.
| |
| |-
| |
| | pow(other[, level, fill_value, axis])
| |
| | Return Exponential power of series and other, element-wise (binary operator pow).
| |
| |-
| |
| | prod([axis, skipna, level, numeric_only, …])
| |
| | Return the product of the values for the requested axis.
| |
| |-
| |
| | product([axis, skipna, level, numeric_only, …])
| |
| | Return the product of the values for the requested axis.
| |
| |-
| |
| | quantile([q, interpolation])
| |
| | Return value at the given quantile.
| |
| |-
| |
| | radd(other[, level, fill_value, axis])
| |
| | Return Addition of series and other, element-wise (binary operator radd).
| |
| |-
| |
| | rank([axis, method, numeric_only, …])
| |
| | Compute numerical data ranks (1 through n) along axis.
| |
| |-
| |
| | ravel([order])
| |
| | Return the flattened underlying data as an ndarray.
| |
| |-
| |
| | rdiv(other[, level, fill_value, axis])
| |
| | Return Floating division of series and other, element-wise (binary operator rtruediv).
| |
| |-
| |
| | rdivmod(other[, level, fill_value, axis])
| |
| | Return Integer division and modulo of series and other, element-wise (binary operator rdivmod).
| |
| |-
| |
| | reindex([index])
| |
| | Conform Series to new index with optional filling logic.
| |
| |-
| |
| | reindex_like(other[, method, copy, limit, …])
| |
| | Return an object with matching indices as other object.
| |
| |-
| |
| | rename([index, axis, copy, inplace, level, …])
| |
| | Alter Series index labels or name.
| |
| |-
| |
| | rename_axis(**kwargs)
| |
| | Set the name of the axis for the index or columns.
| |
| |-
| |
| | reorder_levels(order)
| |
| | Rearrange index levels using input order.
| |
| |-
| |
| | repeat(repeats[, axis])
| |
| | Repeat elements of a Series.
| |
| |-
| |
| | replace([to_replace, value, inplace, limit, …])
| |
| | Replace values given in to_replace with value.
| |
| |-
| |
| | resample(rule[, axis, closed, label, …])
| |
| | Resample time-series data.
| |
| |-
| |
| | reset_index([level, drop, name, inplace])
| |
| | Generate a new DataFrame or Series with the index reset.
| |
| |-
| |
| | rfloordiv(other[, level, fill_value, axis])
| |
| | Return Integer division of series and other, element-wise (binary operator rfloordiv).
| |
| |-
| |
| | rmod(other[, level, fill_value, axis])
| |
| | Return Modulo of series and other, element-wise (binary operator rmod).
| |
| |-
| |
| | rmul(other[, level, fill_value, axis])
| |
| | Return Multiplication of series and other, element-wise (binary operator rmul).
| |
| |-
| |
| | rolling(window[, min_periods, center, …])
| |
| | Provide rolling window calculations.
| |
| |-
| |
| | round([decimals])
| |
| | Round each value in a Series to the given number of decimals.
| |
| |-
| |
| | rpow(other[, level, fill_value, axis])
| |
| | Return Exponential power of series and other, element-wise (binary operator rpow).
| |
| |-
| |
| | rsub(other[, level, fill_value, axis])
| |
| | Return Subtraction of series and other, element-wise (binary operator rsub).
| |
| |-
| |
| | rtruediv(other[, level, fill_value, axis])
| |
| | Return Floating division of series and other, element-wise (binary operator rtruediv).
| |
| |-
| |
| | sample([n, frac, replace, weights, …])
| |
| | Return a random sample of items from an axis of object.
| |
| |-
| |
| | searchsorted(value[, side, sorter])
| |
| | Find indices where elements should be inserted to maintain order.
| |
| |-
| |
| | sem([axis, skipna, level, ddof, numeric_only])
| |
| | Return unbiased standard error of the mean over requested axis.
| |
| |-
| |
| | set_axis(labels[, axis, inplace])
| |
| | Assign desired index to given axis.
| |
| |-
| |
| | shift([periods, freq, axis, fill_value])
| |
| | Shift index by desired number of periods with an optional time freq.
| |
| |-
| |
| | skew([axis, skipna, level, numeric_only])
| |
| | Return unbiased skew over requested axis.
| |
| |-
| |
| | slice_shift([periods, axis])
| |
| | Equivalent to shift without copying data.
| |
| |-
| |
| | sort_index([axis, level, ascending, …])
| |
| | Sort Series by index labels.
| |
| |-
| |
| | sort_values([axis, ascending, inplace, …])
| |
| | Sort by the values.
| |
| |-
| |
| | sparse
| |
| | alias of pandas.core.arrays.sparse.accessor.SparseAccessor
| |
| |-
| |
| | squeeze([axis])
| |
| | Squeeze 1 dimensional axis objects into scalars.
| |
| |-
| |
| | std([axis, skipna, level, ddof, numeric_only])
| |
| | Return sample standard deviation over requested axis.
| |
| |-
| |
| | str
| |
| | alias of pandas.core.strings.StringMethods
| |
| |-
| |
| | sub(other[, level, fill_value, axis])
| |
| | Return Subtraction of series and other, element-wise (binary operator sub).
| |
| |-
| |
| | subtract(other[, level, fill_value, axis])
| |
| | Return Subtraction of series and other, element-wise (binary operator sub).
| |
| |-
| |
| | sum([axis, skipna, level, numeric_only, …])
| |
| | Return the sum of the values for the requested axis.
| |
| |-
| |
| | swapaxes(axis1, axis2[, copy])
| |
| | Interchange axes and swap values axes appropriately.
| |
| |-
| |
| | swaplevel([i, j, copy])
| |
| | Swap levels i and j in a MultiIndex.
| |
| |-
| |
| | tail([n])
| |
| | Return the last n rows.
| |
| |-
| |
| | take(indices[, axis, is_copy])
| |
| | Return the elements in the given positional indices along an axis.
| |
| |-
| |
| | to_clipboard([excel, sep])
| |
| | Copy object to the system clipboard.
| |
| |-
| |
| | to_csv([path_or_buf, sep, na_rep, …])
| |
| | Write object to a comma-separated values (csv) file.
| |
| |-
| |
| | to_dict([into])
| |
| | Convert Series to {label -> value} dict or dict-like object.
| |
| |-
| |
| | to_excel(excel_writer[, sheet_name, na_rep, …])
| |
| | Write object to an Excel sheet.
| |
| |-
| |
| | to_frame([name])
| |
| | Convert Series to DataFrame.
| |
| |-
| |
| | to_hdf(path_or_buf, key[, mode, complevel, …])
| |
| | Write the contained data to an HDF5 file using HDFStore.
| |
| |-
| |
| | to_json([path_or_buf, orient, date_format, …])
| |
| | Convert the object to a JSON string.
| |
| |-
| |
| | to_latex([buf, columns, col_space, header, …])
| |
| | Render object to a LaTeX tabular, longtable, or nested table/tabular.
| |
| |-
| |
| | to_list()
| |
| | Return a list of the values.
| |
| |-
| |
| | to_markdown([buf, mode, index])
| |
| | Print Series in Markdown-friendly format.
| |
| |-
| |
| | to_numpy([dtype, copy, na_value])
| |
| | A NumPy ndarray representing the values in this Series or Index.
| |
| |-
| |
| | to_period([freq, copy])
| |
| | Convert Series from DatetimeIndex to PeriodIndex.
| |
| |-
| |
| | to_pickle(path[, compression, protocol])
| |
| | Pickle (serialize) object to file.
| |
| |-
| |
| | to_sql(name, con[, schema, if_exists, …])
| |
| | Write records stored in a DataFrame to a SQL database.
| |
| |-
| |
| | to_string([buf, na_rep, float_format, …])
| |
| | Render a string representation of the Series.
| |
| |-
| |
| | to_timestamp([freq, how, copy])
| |
| | Cast to DatetimeIndex of Timestamps, at beginning of period.
| |
| |-
| |
| | to_xarray()
| |
| | Return an xarray object from the pandas object.
| |
| |-
| |
| | tolist()
| |
| | Return a list of the values.
| |
| |-
| |
| | transform(func[, axis])
| |
| | Call func on self producing a Series with transformed values.
| |
| |-
| |
| | transpose(*args, **kwargs)
| |
| | Return the transpose, which is by definition self.
| |
| |-
| |
| | truediv(other[, level, fill_value, axis])
| |
| | Return Floating division of series and other, element-wise (binary operator truediv).
| |
| |-
| |
| | truncate([before, after, axis, copy])
| |
| | Truncate a Series or DataFrame before and after some index value.
| |
| |-
| |
| | tshift([periods, freq, axis])
| |
| | (DEPRECATED) Shift the time index, using the index’s frequency if available.
| |
| |-
| |
| | tz_convert(tz[, axis, level, copy])
| |
| | Convert tz-aware axis to target time zone.
| |
| |-
| |
| | tz_localize(tz[, axis, level, copy, …])
| |
| | Localize tz-naive index of a Series or DataFrame to target time zone.
| |
| |-
| |
| | unique()
| |
| | Return unique values of Series object.
| |
| |-
| |
| | unstack([level, fill_value])
| |
| | Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
| |
| |-
| |
| | update(other)
| |
| | Modify Series in place using values from passed Series.
| |
| |-
| |
| | value_counts([normalize, sort, ascending, …])
| |
| | Return a Series containing counts of unique values.
| |
| |-
| |
| | var([axis, skipna, level, ddof, numeric_only])
| |
| | Return unbiased variance over requested axis.
| |
| |-
| |
| | view([dtype])
| |
| | Create a new view of the Series.
| |
| |-
| |
| | where(cond[, other, inplace, axis, level, …])
| |
| | Replace values where the condition is False.
| |
| |-
| |
| | xs(key[, axis, level, drop_level])
| |
| | Return cross-section from the Series/DataFrame.
| |
| |} | | |} |
| {{了解更多|[https://pandas.pydata.org/docs/reference/api/pandas.Series.html#pandas.Series Pandas API:pandas.Series]}}
| |
| ===DataFrame===
| |
| DataFrame是有标记的二维的数据结构,具有可能不同类型的列。由数据,行标签(索引,index),列标签(列,columns)构成。您可以将其视为电子表格或SQL表,或Series对象的字典。它通常是最常用的Pandas对象。
| |
|
| |
| {{了解更多|[https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe pandas文档:用户指南 - DataFrame]}}
| |
| ====创建DataFrame====
| |
| 创建DataFrame对象有多种方法:
| |
| * 使用<code>pandas.DataFrame()</code>构造方法
| |
| * 使用<code>pandas.DataFrame.from_dict()</code>方法,类似构造方法
| |
| * 使用<code>pandas.DataFrame.from_records()</code>方法,类似构造方法
| |
| * 使用函数从导入文件创建,如使用<code>pandas.read_csv()</code>函数导入csv文件创建一个DataFrame对象。
| |
|
| |
| 构造方法<code>pandas.DataFrame()</code>的格式为:
| |
| pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
| |
|
| |
|
| ==Pandas绘图== | | ==Pandas绘图== |