DataFrame.to_string(buf: Optional[IO[str]] =None, columns: Optional[Sequence[Union[Any, Tuple[Any, …]]] =None, col_space: Union[str, int, Dict[Union[Any, Tuple[Any, …]], Union[str, int]],None] =None, header: bool =True, index: bool =True, na_rep: str ='NaN', formatters...
DataFrame.to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, min_rows=None, max_cols=None, show_dimensions=False, decimal='.', line_width=None, m...
DataFrame.to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, min_rows=None, max_cols=None, show_dimensions=False, decimal='.', line_width=None, m...
可以看到，直接用to_dict()函数转换DataFrame，转换后的字典形式如下：{column:{index:value}}。字典的...
>>> df.to_records(index_dtypes="<S2") rec.array([(b'a',1,0.5), (b'b',2,0.75)], dtype=[('I','S2'), ('A','<i8'), ('B','<f8')])>>> index_dtypes = f"<S{df.index.str.len().max()}">>> df.to_records(index_dtypes=index_dtypes) ...
Pandas DataFrame - to_string() function: The to_string() function is used to render a DataFrame to a console-friendly tabular output.
返回:str(或 unicode,取决于数据和选项) 示例#1:使用 DataFrame.to_string() 函数将给定的 DataFrame 呈现为控制台友好的表格输出。不要在输出中包含索引标签。 # importing pandas as pd importpandasaspd # Creating the DataFrame df=pd.DataFrame({'Weight':[45,88,56,15,71], ...
to_string是Pandas中一个非常直观的方法,可以将DataFrame转换为字符串形式。这个方法默认会将DataFrame的每个元素转换为字符串,并按照表格的形式进行排版。 importpandasaspd# 创建一个简单的DataFramedf=pd.DataFrame({'A':[1,2,3],'B':['a','b','c']})# 使用to_string方法转换df_str=df.to_string()pri...
the truncation but when trying to left justify columns with df.to_string(justify='left'), that same display setting somehow pads columns on the left so they are not left aligned. Is there any present way to prevent truncation and get left justified string columns when output to a terminal?
importnumpy as np from sklearn.datasetsimportload_iris importpandas as pd data = load_iris() df = pd.DataFrame(data.data, columns=data.feature_names) # Converts the dataframe into str object with formatting print(df.to_markdown())