• Pandas---DataFrame函数说明

    文章目录


    DataFrame表示的是矩阵的数据表，它包含已排序的列集合，每一列可以是不同的值类型（数值、字符串、布尔值等）。DataFrame既有行索引也有列索引。在DataFrame中，数据被存储为一个以上的二维块，而不是列表、字典等其他一维数组。

    构造函数

    DataFrame([data, index, columns, dtype, copy])

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    属性描述

    属性描述
    DataFrame.index行标签
    DataFrame.columns()返回一个string类型的数组，返回值是所有列的名字。
    DataFrame.dtypes返回一个string类型的二维数组，返回值是所有列的名字以及类型。
    DataFrame.info([verbose, buf, max_cols, …])查看索引、数据类型和内存信息
    DataFrame.select_dtypes([include, exclude])根据数据类型选取子数据框
    DataFrame.values根据数据类型选取子数据框
    DataFrame.axes返回横纵坐标的标签名
    DataFrame.ndim返回该数据集的维度
    DataFrame.size返回数据集元素的个数
    DataFrame.shape返回数据框的行数和列数
    DataFrame.memory_usage([index, deep])每一列的存储
    DataFrame.empty
    DataFrame.set_flags(*[, copy, …])

    类型转换

    方法描述
    DataFrame.astype(dtype[, copy, errors])转换数据类型
    DataFrame.convert_dtypes([infer_objects, …])列标签
    DataFrame.infer_objects()返回数据的类型
    DataFrame.copy([deep])deep深度复制数据
    DataFrame.bool()

    索引和迭代

    方法描述
    DataFrame.head([n])返回前n行数据
    DataFrame.at快速标签常量访问器
    DataFrame.iat快速整型常量访问器
    DataFrame.loc标签定位
    DataFrame.iloc整型定位
    DataFrame.insert(loc, column, value[, …])在特殊地点插入行
    DataFrame.iter()
    DataFrame.items()
    DataFrame.iteritems()返回列名和序列的迭代器
    DataFrame.keys()
    DataFrame.iterrows()返回索引和序列的迭代器
    DataFrame.itertuples([index, name])
    DataFrame.lookup(row_labels, col_labels)
    DataFrame.pop(item)返回删除的项目
    DataFrame.tail([n])返回最后n行
    DataFrame.xs(key[, axis, level, drop_level])
    DataFrame.get(key[, default])
    DataFrame.isin(values)计算表示每一个值是否在传值容器中的布尔数组。
    DataFrame.where(cond[, other, inplace, …])条件筛选
    DataFrame.mask(cond[, other, inplace, axis, …])
    DataFrame.query(expr[, inplace])

    示例：

    df.iloc[0]  # 按位置选取数据
df.iloc['index_one']  # 按索引选取数据
df.iloc[0,:]  # 返回第一行
df.iloc[0, 0]  # 返回第一列的第一个元素
df.ix[[:5], ["col1", "col2"]]  # 返回字段为col1和col2的前5条数据
df.at[5, "col1"]  # 返回索引名称为5，字段名称为col1的数据
df.iat[5, 0]  # 选择索引排序为5，字段排序为0的数据

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    二元运算

    方法描述
    DataFrame.add(other[, axis, level, fill_value])
    DataFrame.sub(other[, axis, level, fill_value])
    DataFrame.mul(other[, axis, level, fill_value])
    DataFrame.div(other[, axis, level, fill_value])
    DataFrame.truediv(other[, axis, level, …])
    DataFrame.floordiv(other[, axis, level, …])
    DataFrame.mod(other[, axis, level, fill_value])
    DataFrame.pow(other[, axis, level, fill_value])
    DataFrame.dot(other)
    DataFrame.radd(other[, axis, level, fill_value])
    DataFrame.rsub(other[, axis, level, fill_value])
    DataFrame.rmul(other[, axis, level, fill_value])
    DataFrame.rdiv(other[, axis, level, fill_value])
    DataFrame.rtruediv(other[, axis, level, …])
    DataFrame.rfloordiv(other[, axis, level, …])
    DataFrame.rmod(other[, axis, level, fill_value])
    DataFrame.rpow(other[, axis, level, fill_value])
    DataFrame.lt(other[, axis, level])
    DataFrame.gt(other[, axis, level])
    DataFrame.le(other[, axis, level])
    DataFrame.ge(other[, axis, level])
    DataFrame.ne(other[, axis, level])
    DataFrame.eq(other[, axis, level])
    DataFrame.combine(other, func[, fill_value, …])
    DataFrame.combine_first(other)

    函数应用&分组&窗口

    方法描述
    DataFrame.apply(func[, axis, raw, …])
    DataFrame.applymap(func[, na_action])
    DataFrame.pipe(func, *args, **kwargs)
    DataFrame.agg([func, axis])
    DataFrame.aggregate([func, axis])
    DataFrame.transform(func[, axis])
    DataFrame.groupby([by, axis, level, …])
    DataFrame.rolling(window[, min_periods, …])
    DataFrame.expanding([min_periods, center, …])
    DataFrame.ewm([com, span, halflife, alpha, …])

    计算统计

    方法描述
    DataFrame.abs()返回前n行数据
    DataFrame.all([axis, bool_only, skipna, level])
    DataFrame.any([axis, bool_only, skipna, level])
    DataFrame.clip([lower, upper, axis, inplace])
    DataFrame.corr([method, min_periods])
    DataFrame.corrwith(other[, axis, drop, method])
    DataFrame.count([axis, level, numeric_only])非NA值的个数
    DataFrame.cov([min_periods, ddof])
    DataFrame.cummax([axis, skipna])累计值的最大值
    DataFrame.cummin([axis, skipna])累计值的最小值
    DataFrame.cumprod([axis, skipna])值得累计积
    DataFrame.cumsum([axis, skipna])累计值
    DataFrame.describe([percentiles, include, …])计算Series或DataFrame各列的汇总统计集合。
    DataFrame.diff([periods, axis])计算第一个算数差值（对时间序列有用）
    DataFrame.eval(expr[, inplace])
    DataFrame.kurt([axis, skipna, level, …])样本峰度（第四时刻）得值
    DataFrame.kurtosis([axis, skipna, level, …])
    DataFrame.mad([axis, skipna, level])平均值的平均绝对差值
    DataFrame.mean([axis, skipna, level, …])均值
    DataFrame.median([axis, skipna, level, …])中位数（50%分位数）
    DataFrame.min([axis, skipna, level, …])计算最小值
    DataFrame.mode([axis, numeric_only, dropna])
    DataFrame.pct_change([periods, fill_method, …])计算百分比
    DataFrame.prod([axis, skipna, level, …])所有值的积
    DataFrame.product([axis, skipna, level, …])
    DataFrame.quantile([q, axis, numeric_only, …])计算样本的0从1间分位数
    DataFrame.rank([axis, method, numeric_only, …])
    DataFrame.round([decimals])
    DataFrame.sem([axis, skipna, level, ddof, …])
    DataFrame.skew([axis, skipna, level, …])样本偏度（第三时刻）值
    DataFrame.sum([axis, skipna, level, …])加和
    DataFrame.std([axis, skipna, level, ddof, …])值的样本标准差
    DataFrame.var([axis, skipna, level, ddof, …])值的样本方差
    DataFrame.nunique([axis, dropna])计算索引的唯一值序列
    DataFrame.value_counts([subset, normalize, …])

    重新索引/选择/标签操作

    方法描述
    DataFrame.add_prefix(prefix)
    DataFrame.add_suffix(suffix)
    DataFrame.align(other[, join, axis, level, …])
    DataFrame.at_time(time[, asof, axis])
    DataFrame.between_time(start_time, end_time)
    DataFrame.drop([labels, axis, index, …])根据传参删除指定索引值，并产生新的索引值。
    DataFrame.drop_duplicates([subset, keep, …])
    DataFrame.duplicated([subset, keep])
    DataFrame.equals(other)
    DataFrame.filter([items, like, regex, axis])
    DataFrame.first(offset)
    DataFrame.head([n])
    DataFrame.idxmax([axis, skipna])
    DataFrame.idxmin([axis, skipna])
    DataFrame.last(offset)
    DataFrame.reindex([labels, index, columns, …])
    DataFrame.reindex_like(other[, method, …])
    DataFrame.rename([mapper, index, columns, …])
    DataFrame.rename_axis([mapper, index, …])
    DataFrame.reset_index([level, drop, …])
    DataFrame.sample([n, frac, replace, …])
    DataFrame.set_axis(labels[, axis, inplace])
    DataFrame.set_index(keys[, drop, append, …])
    DataFrame.tail([n])
    DataFrame.take(indices[, axis, is_copy])
    DataFrame.truncate([before, after, axis, copy])

    处理缺失值

    方法描述
    DataFrame.backfill([axis, inplace, limit, …])
    DataFrame.bfill([axis, inplace, limit, downcast])
    DataFrame.dropna([axis, how, thresh, …])
    DataFrame.ffill([axis, inplace, limit, downcast])
    DataFrame.fillna([value, method, axis, …])
    DataFrame.interpolate([method, axis, limit, …])
    DataFrame.isna()
    DataFrame.isnull()
    DataFrame.notna()
    DataFrame.notnull()
    DataFrame.pad([axis, inplace, limit, downcast])
    DataFrame.replace([to_replace, value, …])

    重新定型、排序、换位

    方法描述
    DataFrame.backfill([axis, inplace, limit, …])
    DataFrame.droplevel(level[, axis])
    DataFrame.pivot([index, columns, values])
    DataFrame.pivot_table([values, index, …])
    DataFrame.reorder_levels(order[, axis])
    DataFrame.sort_values(by[, axis, ascending, …])
    DataFrame.sort_index([axis, level, …])
    DataFrame.nlargest(n, columns[, keep])
    DataFrame.nsmallest(n, columns[, keep])
    DataFrame.swaplevel([i, j, axis])
    DataFrame.stack([level, dropna])
    DataFrame.unstack([level, fill_value])
    DataFrame.swapaxes(axis1, axis2[, copy])
    DataFrame.melt([id_vars, value_vars, …])
    DataFrame.explode(column[, ignore_index])
    DataFrame.squeeze([axis])
    DataFrame.to_xarray()
    DataFrame.T
    DataFrame.transpose(*args[, copy])

    结合/比较/加入/合并

    方法描述
    DataFrame.append(other[, ignore_index, …])将额外的索引对象粘贴到原索引后，产生一个新的索引。
    DataFrame.assign(**kwargs)
    DataFrame.compare(other[, align_axis, …])
    DataFrame.join(other[, on, how, lsuffix, …])
    DataFrame.merge(right[, how, on, left_on, …])
    DataFrame.update(other[, join, overwrite, …])

    时间序列

    方法描述
    DataFrame.asfreq(freq[, method, how, …])
    DataFrame.asof(where[, subset])
    DataFrame.shift([periods, freq, axis, …])
    DataFrame.slice_shift([periods, axis])
    DataFrame.tshift([periods, freq, axis])
    DataFrame.first_valid_index()
    DataFrame.last_valid_index()
    DataFrame.resample(rule[, axis, closed, …])
    DataFrame.to_period([freq, axis, copy])
    DataFrame.to_timestamp([freq, how, axis, copy])
    DataFrame.tz_convert(tz[, axis, level, copy])
    DataFrame.tz_localize(tz[, axis, level, …])

    标志Flags

    方法描述
    Flags(obj, *, allows_duplicate_labels)

    元数据Metadata

    方法描述
    DataFrame.attrs

    作图

    方法描述
    DataFrame.plot([x, y, kind, ax, …])
    DataFrame.plot.area([x, y])
    DataFrame.plot.bar([x, y])
    DataFrame.plot.barh([x, y])
    DataFrame.plot.box([by])
    DataFrame.plot.density([bw_method, ind])
    DataFrame.plot.hexbin(x, y[, C, …])
    DataFrame.plot.hist([by, bins])
    DataFrame.plot.kde([bw_method, ind])
    DataFrame.plot.line([x, y])
    DataFrame.plot.pie(**kwargs)
    DataFrame.plot.scatter(x, y[, s, c])
    DataFrame.boxplot([column, by, ax, …])
    DataFrame.hist([column, by, grid, …])

    Sparse accessor

    DataFrame.sparse

    方法描述
    DataFrame.sparse.density
    DataFrame.sparse.from_spmatrix(data[, …])
    DataFrame.sparse.to_coo()
    DataFrame.sparse.to_dense()

    Serialization / IO / conversion

    方法描述
    DataFrame.from_dict(data[, orient, dtype, …])
    DataFrame.from_records(data[, index, …])
    DataFrame.to_parquet([path, engine, …])
    DataFrame.to_pickle(path[, compression, …])
    DataFrame.to_csv([path_or_buf, sep, na_rep, …])
    DataFrame.to_hdf(path_or_buf, key[, mode, …])
    DataFrame.to_sql(name, con[, schema, …])
    DataFrame.to_dict([orient, into])
    DataFrame.to_excel(excel_writer[, …])
    DataFrame.to_json([path_or_buf, orient, …])
    DataFrame.to_html([buf, columns, col_space, …])
    DataFrame.to_feather(path, **kwargs)
    DataFrame.to_latex([buf, columns, …])
    DataFrame.to_stata(path[, convert_dates, …])
    DataFrame.to_gbq(destination_table[, …])
    DataFrame.to_records([index, column_dtypes, …])
    DataFrame.to_string([buf, columns, …])
    DataFrame.to_clipboard([excel, sep])
    DataFrame.to_markdown([buf, mode, index, …])
    DataFrame.style
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  • 原文地址：https://blog.csdn.net/weixin_43956958/article/details/126475613