data={'website':['pandasdataframe.com','example.com','test.com'],'visits':[1000,1500,800]}df=pd.DataFrame(data)defcalculate_score(row):if'pandasdataframe.com'inrow['website']:returnrow['visits']*1.2else:returnrow['visits']df['score']=df.apply(calculate_score,axis=1)print(df) Pyth...
在操作DataFrame的函数中,通常有沿着轴来进行操作,沿着axis=0,表示对一列(column)的数据进行操作;沿着axis=1,表示对一行(row)的数据进行操作。 axis{0 or ‘index’, 1 or ‘columns’}, default 0 Axis along which the function is applied: 0 or ‘index’: apply function to each column. 1 or ‘c...
在操作DataFrame的函数中,通常有沿着轴来进行操作,沿着axis=0,表示对一列(column)的数据进行操作;沿着axis=1,表示对一行(row)的数据进行操作。 axis{0 or ‘index’, 1 or ‘columns’}, default 0 Axis along which the function is applied: 0 or ‘index’: apply function to each column. 1 or ‘c...
Function to usefortransforming the data. If a function, must either work when passed a Seriesorwhen passed to Series.apply. Accepted combinations are: function string function name list of functionsand/orfunction names, e.g. [np.exp.'sqrt'] dict of axis labels-> functions, function namesor...
2.4 apply_rows和apply_chunks 2.5 groupby 1 cuDF背景与安装 1.1 背景 cuDF在过去一年中的发展速度非常之快。每个版本都加入了令人兴奋的新功能、优化和错误修复。0.10版本也不例外。cuDF 0.10版本的一些新功能包括 groupby.quantile()、Series.isin()、从远程/云文件系统(例如hdfs、gcs、s3)读取、Series和DataFrame...
今天我们学习多个DataFrame之间的连接和追加的操作，在合并DataFrame时，您可能会考虑很多目标。例如，您可能...
Calling drop with a sequence of labels will drop values from either axis. To illustrate this, we first create an example DataFrame: ->(删除某个行标签, 将会对应删掉该行数据) 'drop([row_name1, row_name2]), 删除行, 非原地'data.drop(['Colorado','Ohio']) ...
apply() function as a Series method Applies a function to each element in the Series In [10]: # say we want to calculate length of string in each string in "Name" column# create new column# we are applying Python's len functiontrain['Name_length']=train.Name.apply(len) ...
applymap() Execute a function for each element in the DataFrame apply() Apply a function to one of the axis of the DataFrame assign() Assign new columns astype() Convert the DataFrame into a specified dtype at Get or set the value of the item with the specified label axes Returns the...
Input data: >>> df PAR0 [[1.2.3, 2.3.4]]1 [[3.2, 3.2]] 一步取消列表*并删除重复项: df["PAR"] = df["PAR"].str[0].apply(np.unique) Output data: >>> df PAR0 [1.2.3, 2.3.4]1 [3.2] *在@SeanBean的帮助下更正