示例代码 2: 使用 apply 返回多列 importpandasaspd# 创建一个 DataFramedf=pd.DataFrame({'A':range(1,6),'B':['pandasdataframe.com'for_inrange(5)]})# 定义一个函数,返回多个新的列值defmultiple_columns(row):returnpd.Series([row['A']*2,ro
returning multiple columnsfromPandasapply()A12B27dtype:int64 Python Copy 示例3: 在行上使用还原函数。 # Using pandas.DataFrame.apply() on the data frameprint('returning multiple columns from Pandas apply()')dataFrame.apply(numpy.sum,axis=1) Python Copy 输出: returning multiple columnsfromPandasapply...
return pd.DataFrame(array([[1,2]]), columns=['x1', 'x2']) df['size'].astype(int).apply(gimmeMultiple) df['size'].astype(int).apply(gimmeMultipleDf) 返回一个数据帧肯定有它的好处,但有时不是必需的。您可以查看apply()返回的内容,并对函数进行一些操作;) 2014-05-11 19:11:22 您可以...
In Pandas, the apply() function can indeed be used to return multiple columns by returning a pandas Series or DataFrame from the applied function. In this
To apply a function that returns multiple values to rows in pandas DataFrame, we will define a function for performing some operations on the values, and then finally we will return all the values in the form of a series. Note To work with pandas, we need to importpandaspackage fir...
它是一种一维数组对象,包含一个值序列,还有索引功能。 1.通过列表创建Series 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importpandasaspd obj=pd.Series([1,-2,3,-4])# 仅仅由数组构成print(obj) 代码语言:javascript 代码运行次数:0 运行 ...
hexdigest(), 16) h = k % 2147483648 return h 1. Pandas测试 读取数据集,记录该操作耗时: import pandas as pd df_data = pd.read_csv(data_file, names=col_list) 显示原始数据,df_data.head() 运行apply函数,并记录该操作耗时: for col in df_data.columns: df_data[col] = df_data.apply(...
(self, key, value) 1284 ) 1285 1286 check_dict_or_set_indexers(key) 1287 key = com.apply_if_callable(key, self) -> 1288 cacher_needs_updating = self._check_is_chained_assignment_possible() 1289 1290 if key is Ellipsis: 1291 key = slice(None) ~/work/pandas/pandas/pandas/core/...
df.columns() # 查看字段()名称 df.describe() # 查看汇总统计 s.value_counts() # 统计某个值出现次数 df.apply(pd.Series.value_counts) # 查看DataFrame对象中每列的唯值和计数 df.isnull().any() # 查看是否有缺失值 df[df[column_name].duplicated()] # 查看column_name字段数据重复的数据信息 ...
Yields below output. When you apply count on the entire DataFrame, pretty much all columns will have the same values. So when you want togroup by countjustselect a column, you can even select from your group columns. # Group by multiple columns and get ...