The singular form dtype is used to check the data type for a single column. And the plural form dtypes is for data frame which returns data types for all columns. Essentially: import pandas as pd df = pd.DataFrame ( {'A': [1,2,3], 'B': [True, False, False], 'C': ['a', 'b', 'c']}) df.A.dtype # dtype ('int64') df.B.dtype # dtype ('bool') df.C To display all dataframe columns in a Jupyter Python Notebook, you can use the pd.set_option () function from the Pandas library. This function allows you to set various options for displaying dataframes, including the maximum number of columns that are displayed. train.select_dtypes(include=None, exclude=None) train.select_dtypes(include='number') #will include all the numeric types Referred from Jupyter Notebook. To select all numeric types, use np.number or 'number' To select strings you must use the object dtype but note that this will return all object dtype columns Getting started. First, you need to make sure that ipywidgets is installed in your environment. This will depend a bit on which Jupyter environment you are using. For older Jupyter and JupyterLab installs, make sure to check the details in the docs. But for a basic install, just use pip. pip install ipywidgets. We were able to figure out how to solve the Jupyter Display All Columns code by looking at a range of other samples. How do you show all rows and columns in Jupyter notebook? By default, Jupyter notebooks only displays 20 columns of a pandas DataFrame. You can easily force the notebook to show all columns by using the following syntax: pd. The Output widget can capture and display stdout, stderr and rich output generated by IPython. You can also append output directly to an output widget, or clear it programmatically. out = widgets.Output(layout={'border': '1px solid black'}) out. After the widget is created, direct output to it using a context manager. 60. try: pandas.set_option ('display.max_columns', None) but depending how many columns you have this is not a good idea. The data is being abbreviated because you have too many columns to fit practically on the screen. You might be better off saving to a .csv to inspect the data. df.to_csv ('myfile.csv') I used to have the same problem. I could not find anything that would help me so I ended up making the class PrintTable--code below.There is also an output. The usage is simple: 6IJIc2A.