pd.read_csv("../data/user_info.csv", index_col="name") #假设csv里包含这几列： name, age, birth, sex data="name,age,birth,sex\nTom,18.0,2000-02-10,\nBob,30.0,1988-10-17,male" print(data) pd.read_csv(StringIO(data))#从 StringIO 对象中读取。 data = "name|age|birth|sex~Tom|18.0|2000-02-10|~Bob|30.0|1988-10-17|male" pd.read_csv(StringIO(data), sep="|", lineterminator="~") #自定义字段之间的分隔符 pd.read_csv(StringIO(data), sep="|", lineterminator="~", dtype={"age": int}) # 自己指定数据类型 data="Tom,18.0,2000-02-10,\nBob,30.0,1988-10-17,male" pd.read_csv(StringIO(data), names=["name", "age", "birth", "sex"]) csv文件并没有标题，我们可以设置参数 names 来添加标题。 pd.read_csv(StringIO(data), usecols=["name", "age"]) # 只读取部分列 print(user_info.to_json()) #将dataframe转成json字符串
| 格式类型 | 数据描述 | Reader | Writer |
|---|---|---|---|
| text | CSV | read_csv | to_csv |
| text | JSON | read_json | to_json |
| text | HTML | read_html | to_html |
| text | clipboard | read_clipboard | to_clipboard |
| binary | Excel | read_excel | to_excel |
| binary | HDF5 | read_hdf | to_hdf |
| binary | Feather | read_feather | to_feather |
| binary | Msgpack | read_msgpack | to_msgpack |
| binary | Stata | read_stata | to_stata |
| binary | SAS | read_sas | |
| binary | Python Pickle | read_pickle | to_pickle |
| SQL | SQL | read_sql | to_sql |
| SQL | Google Big Query | read_gbq | to_gbq |
| to_json | |
|---|---|
| split | 字典像索引 - > [索引]，列 - > [列]，数据 - > [值]} |
| records | 列表像{[列 - >值}，…，{列 - >值}] |
| index | 字典像{索引 - > {列 - >值}} |
| columns | 字典像{列 - > {索引 - >值}} |
| values | 只是值数组 |
原文：https://www.cnblogs.com/testzcy/p/12080546.html