# 默认内连接 result = pd.merge(left, right, on=['key1', 'key2']) 结果: 左连接:按左边的数据进行合并 result = pd.merge(left, right, how='left', on=['key1', 'key2']) 结果: 右连接:按右边的数据进行合并 result = pd.merge(left, right, how='right', on=['key1', 'key2'...
columns.get_level_values(0).astype(str) In[99]: stock_concat Out[99]: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In[100]: step1 = stocks_2016.merge(stocks_2017, left_index=True, right_index=True, how='outer', suffixes=('_2016', '_2017')) stock_merge = step1.merge(...
df.query('A > B') # 等价于df[df.A > df.B] # 确定选取的部门和列 depts = ['Houston Police Department-HPD', 'Houston Fire Department (HFD)'] select_columns = ['UNIQUE_ID', 'DEPARTMENT', 'GENDER', 'BASE_SALARY'] qs = "DEPARTMENT in @depts and GENDER == 'Female' and 80000 <...
columns Index(['one', 'two'], dtype='object') Index Immutable ndarray implementing an ordered, sliceable set. The basic object storing axis labels for all pandas objects. An Index instance can only contain hashable objects. Series和DataFrame都有对应的Index,Index本身是很有趣的数据结构。可以将...
read_excel可以通过将列列表传递给index_col和将行列表传递给header来读取MultiIndex索引。如果index或columns具有序列化级别名称,也可以通过指定构成级别的行/列来读取这些级别。 例如,要读取没有名称的MultiIndex索引: In [424]: df = pd.DataFrame(...: {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]...
DataFrame.merge(right[, how, on, left_on, …])Merge DataFrame objects by performing a database-style join operation by columns or indexes. DataFrame.update(other[, join, overwrite, …])Modify DataFrame in place using non-NA values from passed DataFrame. ...
{f:18}',end='' if i%5 else '\n') boxplot to_html from_dict to_xml info corrwith eval to_parquet to_records join stack columns melt iterrows to_feather applymap to_stata style pivot set_index assign itertuples lookup query select_dtypes from_records insert merge to_gbq pivot_table ...
Theconcat()function (in the main pandas namespace) does all of the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes. Note that I say “if any” because there is only a...
merge函数会根据给定的列连接两个Data Frame,并创建一个新的Data Frame。merge函数的工作方式与关系数据库的join操作类似,给出的是一张数据表,其中组合了来自两个文件的数据列: print(calls_revenue) Team member Territory Month Calls Amount 0 Jorge 3 1 107 50876 1 Jorge 3 2 88 57682 2 Jorge 3 3 84...
df.columns = ['A','B','C'] # 改列名,必须指明每一列否则抛错 df.rename(columns={'a':'A', 'b':'B', 'c':'C'}, inplace=True) # 改列名,指定改某列,推荐 pd.merge(数据1,数据2) # 合并 pd.concat(数据1,数据2) # 合并 ...