Pandas中DataFrame基本函数整理(小结)
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2020-09-16
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Pandas中中DataFrame基本函数整理基本函数整理(小结小结)
主要介绍了Pandas中DataFrame基本函数整理(小结)，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学
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构造函数构造函数
DataFrame([data, index, columns, dtype, copy]) #构造数据框
属性和数据属性和数据
DataFrame.axes #index: 行标签；columns: 列标签
DataFrame.as_matrix([columns]) #转换为矩阵
DataFrame.dtypes #返回数据的类型
DataFrame.ftypes #返回每一列的 数据类型float64:dense
DataFrame.get_dtype_counts() #返回数据框数据类型的个数
DataFrame.get_ftype_counts() #返回数据框数据类型float64:dense的个数
DataFrame.select_dtypes([include, include]) #根据数据类型选取子数据框
DataFrame.values #Numpy的展示方式
DataFrame.axes #返回横纵坐标的标签名
DataFrame.ndim #返回数据框的纬度
DataFrame.size #返回数据框元素的个数
DataFrame.shape #返回数据框的形状
DataFrame.memory_usage() #每一列的存储
类型转换类型转换
DataFrame.astype(dtype[, copy, errors]) #转换数据类型
DataFrame.copy([deep]) #deep深度复制数据
DataFrame.isnull() #以布尔的方式返回空值
DataFrame.notnull() #以布尔的方式返回非空值
索引和迭代索引和迭代
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，使用名称
DataFrame.iloc #整型定位，使用数字
DataFrame.insert(loc, column, value) #在特殊地点loc[数字]插入column[列名]某列数据
DataFrame.iter() #Iterate over infor axis
DataFrame.iteritems() #返回列名和序列的迭代器
DataFrame.iterrows() #返回索引和序列的迭代器
DataFrame.itertuples([index, name]) #Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple.
DataFrame.lookup(row_labels, col_labels) #Label-based “fancy indexing” function for DataFrame.
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]) #Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
DataFrame.isin(values) #是否包含数据框中的元素
DataFrame.where(cond[, other, inplace, …]) #条件筛选
DataFrame.mask(cond[, other, inplace, …]) #Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
DataFrame.query(expr[, inplace]) #Query the columns of a frame with a boolean expression.
二元运算二元运算
DataFrame.add(other[,axis,fill_value]) #加法，元素指向
DataFrame.sub(other[,axis,fill_value]) #减法，元素指向
DataFrame.mul(other[, axis,fill_value]) #乘法，元素指向
DataFrame.div(other[, axis,fill_value]) #小数除法，元素指向
DataFrame.truediv(other[, axis, level, …]) #真除法，元素指向
DataFrame.floordiv(other[, axis, level, …]) #向下取整除法，元素指向
DataFrame.mod(other[, axis,fill_value]) #模运算，元素指向
DataFrame.pow(other[, axis,fill_value]) #幂运算，元素指向
DataFrame.radd(other[, axis,fill_value]) #右侧加法，元素指向
DataFrame.rsub(other[, axis,fill_value]) #右侧减法，元素指向
DataFrame.rmul(other[, axis,fill_value]) #右侧乘法，元素指向
DataFrame.rdiv(other[, axis,fill_value]) #右侧小数除法，元素指向
DataFrame.rtruediv(other[, axis, …]) #右侧真除法，元素指向
DataFrame.rfloordiv(other[, axis, …]) #右侧向下取整除法，元素指向
DataFrame.rmod(other[, axis,fill_value]) #右侧模运算，元素指向
DataFrame.rpow(other[, axis,fill_value]) #右侧幂运算，元素指向
DataFrame.lt(other[, axis, level]) #类似Array.lt
DataFrame.gt(other[, axis, level]) #类似Array.gt
DataFrame.le(other[, axis, level]) #类似Array.le
DataFrame.ge(other[, axis, level]) #类似Array.ge
DataFrame.ne(other[, axis, level]) #类似Array.ne
DataFrame.eq(other[, axis, level]) #类似Array.eq
DataFrame.combine(other,func[,fill_value, …]) #Add two DataFrame objects and do not propagate NaN values, so if for a
DataFrame.combine_first(other) #Combine two DataFrame objects and default to non-null values in frame calling the method.
函数应用函数应用&分组分组&窗口窗口
DataFrame.apply(func[, axis, broadcast, …]) #应用函数
DataFrame.applymap(func) #Apply a function to a DataFrame that is intended to operate elementwise, i.e.
DataFrame.aggregate(func[, axis]) #Aggregate using callable, string, dict, or list of string/callables
DataFrame.transform(func, *args, **kwargs) #Call function producing a like-indexed NDFrame
DataFrame.groupby([by, axis, level, …]) #分组
DataFrame.rolling(window[, min_periods, …]) #滚动窗口
DataFrame.expanding([min_periods, freq, …]) #拓展窗口
DataFrame.ewm([com, span, halflife, …]) #指数权重窗口
描述统计学描述统计学
DataFrame.abs() #返回绝对值
DataFrame.all([axis, bool_only, skipna]) #Return whether all elements are True over requested axis
DataFrame.any([axis, bool_only, skipna]) #Return whether any element is True over requested axis
DataFrame.clip([lower, upper, axis]) #Trim values at input threshold(s).
DataFrame.clip_lower(threshold[, axis]) #Return copy of the input with values below given value(s) truncated.
DataFrame.clip_upper(threshold[, axis]) #Return copy of input with values above given value(s) truncated.
DataFrame.corr([method, min_periods]) #返回本数据框成对列的相关性系数
DataFrame.corrwith(other[, axis, drop]) #返回不同数据框的相关性
DataFrame.count([axis, level, numeric_only]) #返回非空元素的个数
DataFrame.cov([min_periods]) #计算协方差
DataFrame.cummax([axis, skipna]) #Return cumulative max over requested axis.
DataFrame.cummin([axis, skipna]) #Return cumulative minimum over requested axis.
DataFrame.cumprod([axis, skipna]) #返回累积
DataFrame.cumsum([axis, skipna]) #返回累和
DataFrame.describe([percentiles,include, …]) #整体描述数据框
DataFrame.diff([periods, axis]) #1st discrete difference of object
DataFrame.eval(expr[, inplace]) #Evaluate an expression in the context of the calling DataFrame instance.
DataFrame.kurt([axis, skipna, level, …]) #返回无偏峰度Fisher's (kurtosis of normal == 0.0).
DataFrame.mad([axis, skipna, level]) #返回偏差
DataFrame.max([axis, skipna, level, …]) #返回最大值
DataFrame.mean([axis, skipna, level, …]) #返回均值
DataFrame.median([axis, skipna, level, …]) #返回中位数
DataFrame.min([axis, skipna, level, …]) #返回最小值
DataFrame.mode([axis, numeric_only]) #返回众数
DataFrame.pct_change([periods, fill_method]) #返回百分比变化
DataFrame.prod([axis, skipna, level, …]) #返回连乘积
DataFrame.quantile([q, axis, numeric_only]) #返回分位数
DataFrame.rank([axis, method, numeric_only]) #返回数字的排序
DataFrame.round([decimals]) #Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof]) #返回无偏标准误
DataFrame.skew([axis, skipna, level, …]) #返回无偏偏度
DataFrame.sum([axis, skipna, level, …]) #求和
DataFrame.std([axis, skipna, level, ddof]) #返回标准误差
DataFrame.var([axis, skipna, level, ddof]) #返回无偏误差
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