sort_values(by=multiple columns) 比较两个dataframe是否相等 np.where np.log2 + where or and in string regex use | as or df.columns[df.columns.str.contains('rnk|rank') where np.where, condition, if true value, if false value np.where(df.index.isin(idxs),df.index,'') df.col.where...
>>> y = np.array([1,5,6,8,1,7,3,6,9]) # Where y is greater than 5, returns index position >>> np.where(y>5) array([2, 3, 5, 7, 8], dtype=int64) # First will replace the values that match the condition, # second will replace the values that does not >>> np.whe...
limit_area="inside", limit=1) Out[148]: 0 NaN 1 NaN 2 5.0 3 7.0 4 NaN 5 11.0 6 13.0 7 NaN 8 NaN dtype: float64 # fill all consecutive outside values backward In [149]: ser.interpolate(limit_direction="backward", limit_area="outside") Out[149]: 0 5.0 ...
应用布尔索引后，您将获得满足条件的'Value'列的所有行。换句话说，您将获得过滤列，它是一个pandas....
应用布尔索引后，您将获得满足条件的'Value'列的所有行。换句话说，您将获得过滤列，它是一个pandas....
df.where方法 df.query方法 .loc既能查询,又能覆盖写入,强烈推荐! Pandas使用df.loc查询数据的方法 使用单个label值查询数据 使用值列表批量查询 使用数值区间进行范围查询 使用条件表达式查询 调用函数查询 注意 以上查询方法,既适用于行,也适用于列 注意观察降维dataFrame>Series>值 In [23]: 代码语言:javascript ...
get_year = lambda x: x.year #取得年度 by_year = rets.groupby(get_year)#按年分组 by_year.apply(lambda g: g['AAPL'].corr(g['MSFT']))#计算按年分组的相关系数 三、数据透视表(pivot_table) obj.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, marg...
Python program to replace all values in a column, based on condition # Importing pandas packageimportpandasaspd# creating a dictionary of student marksd={"Players":['Sachin','Ganguly','Dravid','Yuvraj','Dhoni','Kohli'],"Format":['ODI','ODI','ODI','ODI','ODI','ODI'],"Runs":[1592...
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index position np.where(y>5) array([2,3,5,7,8], dtype=int64),)# First will replace the values that match the condition, # second will replace the values that does not ...
np.where(y>5) array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition, # second will replace the values that does not np.where(y>5, "Hit", "Miss") array(['Miss', 'Miss', 'Hit', 'Hit', 'Miss', 'Hit', 'Miss', 'Hit', 'Hit'...