pivot_table = data.pivot_table(values='price', index='category', columns='product', aggfunc=np.sum, fill_value=0) print(pivot_table) 这个示例代码中,我们首先使用 Pandas 的 read_csv 函数读取 CSV 文件中的数据,并使用 dropna 函数删除缺失值。然后,我们使用 drop_duplicates 函数删除重复行。接着...
s.replace([1,3],['one','three']) # 'one'代替1,'three'代替3 df.rename(columns=lambdax:x+1) # 批量更改列名 df.rename(columns={'old_name':'new_ name'}) # 选择性更改列名 df.set_index('column_one') # 将某个字段设为索引,可接受列表参数,即设置多个索引 df.reset_index("col1") ...
columns=['feature_one','feature_two','feature_three','feature_four'], index=['one','two','three'] ) # 定义计算函数 # 计算 x 的累加和 def get_sum(x): return x.sum() # 程序入口 if __name__ == '__main__ ': # 计算第 1 列和第 2 列元素的和 result = s_data.iloc[:,0...
(include=['int']).sum(1)df['total'] = df.loc[:,'Q1':'Q4'].apply(lambda x: sum(x), axis='columns') df.loc[:, 'Q10'] = '我是新来的' # 也可以 # 增加一列并赋值,不满足条件的为NaN df.loc[df.num >= 60, '成绩'] = '合格' df.loc[df.num < 60, '成绩'] = '不...
Given a DataFrame, we need to create a new column in which contains sum of values of all the columns row wise.ByPranit SharmaLast updated : September 25, 2023 Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mos...
TotalSales=('Sales','sum'),AverageProfit=('Profit','mean')).sort_values(by='TotalSales',ascending=False)#4.排序.head(5)#5.取前5)print(top_5_subcategories_chained) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 注释:链式操作通过将每个方法调用的结果直接作为下一个方法调用的对象,...
Having specificdtypes In [12]:df2.dtypesOut[12]:A float64B datetime64[ns]C float32D int32E categoryF objectdtype: object If you’re using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Here’s a subset of the attributes that will be ...
Selecting/excluding sets of columns in pandas For this purpose, we useDataFrame.loc[]property with the specific conditions and/or slicing. TheDataFrame.loc[]property will read the sliced index, colon (:) means starting from the first column,DataFrame.columnswill return all the columns of a Data...
columns=df_chunk.columns) # 分块处理10GB级数据 scaled_data = Parallel(n_jobs=4)( delayed(parallel_scale)(chunk) for chunk in np.array_split(big_data, 8) ) 可解释性标准化 PYTHON # 保留原始分布信息 orders['amount_scaled'] = orders['amount'].pipe( ...
For specific summary statistics, Pandas offers individual functions like mean(), median(), std(), var(), min(), max(), quantile(), and sum() which can be applied to columns or rows of a DataFrame. By default, these functions operate column-wise, but with appropriate arguments, they ca...