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Pandas是[[Python]]的一个开源软件库,用于数据分析,可以方便对数据进行处理、计算、分析、存储及可视化。 ==简介== ===时间轴=== *2008年,开发者Wes McKinney在AQR Capital Management开始制作pandas来满足在财务数据上进行定量分析对高性能、灵活工具的需要。在离开AQR之前他说服管理者允许他将这个库开放源代码。 *2012年,另一个AQR雇员Chang She加入了这项努力并成为这个库的第二个主要贡献者。 *2015年,Pandas签约了NumFOCUS的一个财务赞助项目,它是美国的501(c)(3)非营利慈善团体。 ===安装和导入=== 使用pip安装Pandas pip install pandas 如果使用的是Anaconda等计算科学软件包,已经安装好了pandas库。 导入Pandas,在脚本顶部导入,一般写法如下: import pandas as pd 查看Pandas版本: pd.__version__ ==数据结构== pandas定义了2种数据类型,Series和DataFrame,大部分操作都在这两种数据类型上进行。 {{了解更多 |[https://pandas.pydata.org/docs/user_guide/dsintro.html Pandas 用户指南:数据结构] }} ===Series=== Series是一个有轴标签(索引)的一维数组,能够保存任何数据类型(整数,字符串,浮点数,Python对象等)。轴标签称为<code>index</code>。和Python字典类似。 ====创建Series==== 创建Series的基本方法为,使用[[Pandas/pandas.Series|pandas.Series]]类新建一个Series对象,格式如下: pd.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False) 轴标签index不是必须,如果省略,轴标签默认为从0开始的整数数组。一些示例如下: <syntaxhighlight lang="python" > s = pd.Series(["foo", "bar", "foba"]) print(type(s)) #<class 'pandas.core.series.Series'> s2 = pd.Series(["foo", "bar", "foba"], index=['b','d','c']) # 创建日期索引 date_index = pd.date_range("2020-01-01", periods=3, freq="D") s3 = pd.Series(["foo", "bar", "foba"], index=date_index) </syntaxhighlight> ====Series数据操作==== ====Series属性==== 下表示例中s为Series对象: <syntaxhighlight lang="python" > >>> s = pd.Series(['a', 'b', 'c']) >>> s 0 a 1 b 2 c dtype: object </syntaxhighlight> {| class="wikitable" |- !属性名 !描述 !示例 !结果 |- | T | 返回转置,根据定义,Series转置为自身。 | s.T | 自身 |- | array | 返回 Series 或 Index 数据的数组,该数组为pangdas扩展的python数组. | s.array | <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 | 此对象全局属性字典。 | s.attrs | {} |- | axes | 返回行轴标签的列表。 | s.axes | [RangeIndex(start=0, stop=3, step=1)] |- | dtype | 返回数据的Numpy数据类型 | s.dtype | dtype('O') |- | dtypes | 返回数据的Numpy数据类型 | s.dtypes | dtype('O') |- | hasnans | 如果有任何空值(如Python的None,np.NaN)返回True,否则返回False。 | s2 = pd.Series(['a', None, 'c']) <br \>s2.hasnans | True |- | iat | 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' |'b' |- | iloc |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> |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 |- | 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]}} ====Series方法==== {| class="wikitable" |- ! 方法 ! 描述 ! 示例 ! 结果 |- | abs() | 返回 Series/DataFrame 每个元素的绝对值。 | s.abs() | |- | 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,列标签构成。 ==Pandas绘图== pandas绘图基于[[Matplotlib]],pandas的DataFrame和Series都自带生成各类图表的plot方法,能够方便快速生成各种图表。 {{了解更多 |[https://pandas.pydata.org/docs/user_guide/visualization.html pandas文档:用户指南 - 可视化] }} ===基本图形=== ====折线图==== plot方法默认生成的就是折线图。如prices是一个DataFrame的含有收盘价close列,绘制收盘价的折线图: <syntaxhighlight lang="python" > s = prices['close'] s.plot() #设置图片大小,使用figsize参数 s.plot(figsize=(20,10)) </syntaxhighlight> ====条形图==== 对于不连续标签,没有时间序列的数据,可以绘制条形图,使用以下两种方法: *使用plot()函数,设置kind参数为‘bar’ or ‘barh’, *使用plot.bar()函数,plot.barh()函数 <syntaxhighlight lang="python" > df.plot(kind='bar') #假设df为每天股票数据 df.plot.bar() df.resample('A-DEC').mean().volume.plot(kind='bar') #重采集每年成交量平均值,绘制条形图(volume为df的成交量列) df.plot.bar(stacked=True) #stacked=True表示堆积条形图 df.plot.barh(stacked=True) #barh 表示水平条形图 </nowiki> </syntaxhighlight> ====直方图==== 直方图使用plot.hist()方法绘制,一般为频数分布直方图,x轴分区间,y轴为频数。组数用参数bins控制,如分20组bins=20 <syntaxhighlight lang="python" > df.volume.plot.hist() #df股票数据中成交量volume的频数分布直方图。 df.plot.hist(alpha=0.5) #alpha=0.5 表示柱形的透明度为0.5 df.plot.hist(stacked=True, bins=20) #stacked=True表示堆积绘制,bins=20表示分20组。 df.plot.hist(orientation='horizontal') #orientation='horizontal' 表示水平直方图 df.plot.hist(cumulative=True) #表示累计直方图 df['close'].diff().hist() #收盘价上应用diff函数,再绘制直方图 df.hist(color='k', bins=50) #DataFrame.hist函数将每列绘制在不同的子图形上。 </syntaxhighlight> ====箱型图==== 箱型图可以使用plot.box()函数或DataFrame的boxplot()绘制。 参数: *color,用来设置颜色,通过传入颜色字典,如color={'boxes': 'DarkGreen', 'whiskers': 'DarkOrange', 'medians': 'DarkBlue', 'caps': 'Gray'} *sym,用来设置异常值样式,如sym='r+'表示异常值用'红色+'表示。 <syntaxhighlight lang="python" > df.plot.box() df[['close','open', 'high']].plot.box() #改变箱型颜色,通过传入颜色字典 color={'boxes': 'DarkGreen', 'whiskers': 'DarkOrange', 'medians': 'DarkBlue', 'caps': 'Gray'} df.plot.box(color=color, sym='r+') #sym用来设置异常值样式,'r+'表示'红色+' df.plot.box(positions=[1, 4, 5, 6, 8]) #positions表示显示位置,df有5个列, 第一列显示在x轴1上,第二列显示在x轴4上,以此类推 df.plot.box(vert=False) #表示绘制水平箱型图 df.boxplot() #绘制分层箱型图,通过设置by关键词创建分组,再按组,分别绘制箱型图。如下面例子,每列按A组,B组分别绘制箱型图。 df = pd.DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2']) df['x'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B']) df.boxplot(by='x') #还可以再传入一个子分类,再进一步分组绘制。如: df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y']) </syntaxhighlight> ====散点图==== 散点图使用DataFrame.plot.scatter()方法绘制。通过参数x,y指定x轴和y轴的数据列。 <syntaxhighlight lang="python" > df.plot.scatter(x='close', y='volume') #假如df为每日股票数据,图表示收盘价与成交量的散点图 #将两组散点图绘制在一张图表上,重新ax参数如 ax = df.plot.scatter(x='close', y='volume', color='DarkBlue', label='Group 1') #设置标签名label设置标名 df.plot.scatter(x='open', y='value', color='DarkGreen', label='Group 2', ax=ax) #c参数表示圆点的颜色按按volume列大小来渐变表示。 df.plot.scatter(x='close', y='open', c='volume', s=50) #s表示原点面积大小 df.plot.scatter(x='close', y='open', s=df['volume']/50000) #圆点的大小也可以根据某列数值大小相应设置。 </syntaxhighlight> ====饼图==== 饼图使用DataFrame.plot.pie()或Series.plot.pie()绘制。如果数据中有空值,会自动使用0填充。 ===其他绘图函数=== 这些绘图函数来自[https://pandas.pydata.org/pandas-docs/stable/reference/plotting.html pandas.plotting]模块。 ====矩阵散点图(Scatter Matrix Plot)==== 矩阵散点图(Scatter Matrix Plot)使用scatter_matrix()方法绘制 <syntaxhighlight lang="python" > from pandas.plotting import scatter_matrix #使用前需要从模块中导入该函数 scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde') #假设df是每日股票数据,会每一列相对其他每一列生成一个散点图。 </syntaxhighlight> ====密度图(Density Plot)==== 密度图使用Series.plot.kde()和DataFrame.plot.kde()函数。 df.plot.kde() ====安德鲁斯曲线(Andrews Curves)==== 安德鲁斯曲线 ====平行坐标图(Parallel Coordinates)==== ====Lag plot==== ====自相关图(Autocorrelation Plot)==== 自相关图 ====自举图(Bootstrap plot)==== ===绘图格式=== ====预设置图形样式==== matplotlib 从1.5开始,可以预先设置样式,绘图前通过matplotlib.style.use(my_plot_style)。如matplotlib.style.use('ggplot') 定义ggplot-style plots. ====样式参数==== 大多数绘图函数,可以通过一组参数来设置颜色。 ====标签设置==== 可通过设置legend参数为False来隐藏图片标签,如 df.plot(legend=False) ====尺度==== *logy参数用来将y轴设置对数标尺 *logx参数用来将x轴设置对数标尺 *loglog参数用来将x轴和y轴设置对数标尺 ts.plot(logy=True) ====双坐标图==== 两组序列同x轴,但y轴数据不同,可以通过第二个序列设置参数:secondary_y=True,来设置第二个y轴。 <syntaxhighlight lang="python" > #比如想在收盘价图形上显示cci指标: prices['close'].plot() prices['cci'].plot(secondary_y=True) #第二个坐标轴要显示多个,可以直接传入列名 ax = df.plot(secondary_y=['cci', 'RSI'], mark_right=False) #右边轴数据标签默认会加个右边,设置mark_right为False取消显示 ax.set_ylabel('CD scale') #设置左边y轴名称 ax.right_ax.set_ylabel('AB scale') #设置右边y轴名称 </syntaxhighlight> ====子图==== DataFrame的每一列可以绘制在不同的坐标轴(axis)中,使用subplots参数设置,例如: df.plot(subplots=True, figsize=(6, 6)) ====子图布局==== 子图布局使用关键词layout设置, ==资源== ===官网=== *[https://pandas.pydata.org/ Pandas官网] *[https://pandas.pydata.org/docs/ Pandas文档] *[https://pandas.pydata.org/docs/user_guide/10min.html Pandas 用户指南 - 10分钟入门Pandas] *[https://pandas.pydata.org/docs/user_guide/index.html Pandas 用户指南] *[https://pandas.pydata.org/docs/reference/index.html Pandas API参考] *[https://github.com/pandas-dev/pandas Pandas 的 Github] ===相关网站=== *[https://quant.itiger.com/tquant/research/hub/classroom/detail?nid=4 老虎量化:pandas 介绍] *[https://www.pypandas.cn/docs/ pypandas.cn:Pandas文档] *[https://www.yiibai.com/pandas 易百教程:Pandas] ===书籍=== 《利用Python进行数据分析 第2版》 - Wes McKinney ==参考文献== *[https://zh.wikipedia.org/wiki/Pandas 维基百科:Pandas] *[https://en.wikipedia.org/wiki/Pandas_(software) 维基百科:Pandas(英)] [[分类:数据分析]] [[分类:数据可视化]]
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