Frequently Asked Questions on Pandas Explode Multiple Columns Can I use the explode() function in Pandas to explode multiple columns simultaneously? Theexplode()function in Pandas is designed to be applied to a single column at a time. To explode multiple columns, you can use theapplyfunction in...
Use the explode() Function to Explode Multiple Columns in Pandas Use Series.explode to Explode Multiple Columns in Pandas The data we have access to can contain different datatypes, from strings to arrays or lists. Typically we prefer numbers (integers and floats) and strings as there are ...
df.apply(lambda row: '|'.join(filter(pd.notna, [row['source_1'], row['source_2']])), axis=1) sort_values(by=multiple columns) sort_values可以不止一个column,可以多个。 df.sort_values(by=['name', 'number']) 还可以指定ascending=[False, True] 比较两个dataframe是否相等 有的时候df1...
"cat", "long"), ...: ("B", "cat", "long"), ...: ("A", "dog", "short"), ...: ("B", "dog", "short"), ...: ], ...: names=["exp", "animal", "hair_length"], ...: ) ...: In [36]: df = pd.DataFrame(np.random.randn(4, 4), columns=columns) In [3...
In this article, you have learned how to get column Index from a column name by usingget_loc(), andget_indexer(). To get the index for multiple column names pass columns as a list toget_loc()method. Related Articles Pandas Explode Multiple Columns ...
explode():将类似列表的值的列转换为单独的行。 crosstab():计算多个一维因子数组的交叉制表。 cut():将连续变量转换为离散的分类值。 factorize():将一维变量编码为整数标签。 pivot()和pivot_table() pivot() 数据通常以所谓的“堆叠”或“记录”格式存储。在“记录”或“宽”格式中,通常每个主题都有一行。
df.A, vals.shape[1])pd.DataFrame(np.column_stack((a, vals.ravel())), columns=df.columns)...
1.explode() 英文翻译过来就是爆炸、激增的意思。所以顾名思义,explode()函数可以用于将列表或数组列转换为多行,也就是可以将一行变多行。 例如,如果DataFrame具有包含值列表的列,则在该列上调用explode()将为列表中的每个值创建一行新行。新行其他列的值与原始行相同,但爆炸的列则是列表中的单个值。
['text']=df['text'].str.split(';')# 使用 explode 函数将列表中的元素展开成多行,并同时新增一列标识原始列表的下标result_df=df.explode('text',ignore_index=True)result_df['original_index']=result_df.index# 重命名列result_df.columns=['text','original_index']# 最终的拆分后的表格print(...
('Vitamin D','Sources')])# Creating a multilevel index DataFrame# with columns = multilevel indexesdf=pd.DataFrame([['Papaya','Orange','Oily Fish'], ['Watermelon','Blackcurrent','Red meat'], ['Mango','Kale','egg yolks']], columns=index)# Display multilevel DataFrameprint("Multi...