get list of pandas dataframe columns based on data type

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If I have a dataframe with the following columns:

1. NAME                                     object
2. On_Time                                      object
3. On_Budget                                    object
4. %actual_hr                                  float64
5. Baseline Start Date                  datetime64[ns]
6. Forecast Start Date                  datetime64[ns] 

I would like to be able to say: here is a dataframe, give me a list of the columns which are of type Object or of type DateTime?

I have a function which converts numbers (Float64) to two decimal places, and I would like to use this list of dataframe columns, of a particular type, and run it through this function to convert them all to 2dp.

Maybe:

For c in col_list: if c.dtype = "Something"
list[]
List.append(c)?

If you want a list of columns of a certain type, you can use groupby:

>>> df = pd.DataFrame([[1, 2.3456, 'c', 'd', 78]], columns=list("ABCDE"))
>>> df
   A       B  C  D   E
0  1  2.3456  c  d  78

[1 rows x 5 columns]
>>> df.dtypes
A      int64
B    float64
C     object
D     object
E      int64
dtype: object
>>> g = df.columns.to_series().groupby(df.dtypes).groups
>>> g
{dtype('int64'): ['A', 'E'], dtype('float64'): ['B'], dtype('O'): ['C', 'D']}
>>> {k.name: v for k, v in g.items()}
{'object': ['C', 'D'], 'int64': ['A', 'E'], 'float64': ['B']}

How to Get the Column Names from a Pandas Dataframe, How do you determine the data type for a DataFrame column? Get list of pandas dataframe column names based on data type Suppose we want a list of column names whose data type is np.object i.e string. Let’s see how to do that,


As of pandas v0.14.1, you can utilize select_dtypes() to select columns by dtype

In [2]: df = pd.DataFrame({'NAME': list('abcdef'),
    'On_Time': [True, False] * 3,
    'On_Budget': [False, True] * 3})

In [3]: df.select_dtypes(include=['bool'])
Out[3]:
  On_Budget On_Time
0     False    True
1      True   False
2     False    True
3      True   False
4     False    True
5      True   False

In [4]: mylist = list(df.select_dtypes(include=['bool']).columns)

In [5]: mylist
Out[5]: ['On_Budget', 'On_Time']

Python, How do you determine the data type for a data frame? # get datatypes of columns in the dataframe >gapminder.dtypes country object year int64 pop float64 continent object lifeExp float64 gdpPercap float64 dtype: object How To Select Columns with NUmerical Data Types . Pandas select_dtypes function allows us to specify a data type and select columns matching the data type. For example, to select columns with numerical data type, we can use select_dtypes with argument number. Now we get a new data frame with only numerical datatypes.


Using dtype will give you desired column's data type:

dataframe['column1'].dtype

if you want to know data types of all the column at once, you can use plural of dtype as dtypes:

dataframe.dtypes

How to get & check data types of Dataframe columns in Python , Get list of pandas dataframe column names based on data type. Suppose we want a list of column names whose data type is np.object i.e string  get list of pandas dataframe columns based on data type. 0 votes . 1 view. asked Aug 10, 2019 in Data Science by sourav (17.6k points)


You can use boolean mask on the dtypes attribute:

In [11]: df = pd.DataFrame([[1, 2.3456, 'c']])

In [12]: df.dtypes
Out[12]: 
0      int64
1    float64
2     object
dtype: object

In [13]: msk = df.dtypes == np.float64  # or object, etc.

In [14]: msk
Out[14]: 
0    False
1     True
2    False
dtype: bool

You can look at just those columns with the desired dtype:

In [15]: df.loc[:, msk]
Out[15]: 
        1
0  2.3456

Now you can use round (or whatever) and assign it back:

In [16]: np.round(df.loc[:, msk], 2)
Out[16]: 
      1
0  2.35

In [17]: df.loc[:, msk] = np.round(df.loc[:, msk], 2)

In [18]: df
Out[18]: 
   0     1  2
0  1  2.35  c

pandas.DataFrame.select_dtypes, Return a subset of the DataFrame's columns based on the column dtypes. Parameters. include, excludescalar or list-like. A selection of dtypes or strings To select all numeric types, use np.number or 'number'. To select strings you must use  Let’s get the data type of each column in pandas dataframe with dtypes function as shown below ''' data type of each columns''' print(df1.dtypes) So the result will be . Get the datatype of a single column in pandas: Let’s get the data type of single column in pandas dataframe by applying dtypes function on specific column as shown below


df.select_dtypes(['object'])

This should do the trick

get list of pandas dataframe columns based on data type, get list of pandas dataframe columns based on data type. NAME object. On_Time object. On_Budget object. %actual_hr float64. Baseline Start Date datetime64[ns] Forecast Start Date datetime64[ns] How to Convert Dictionary into DataFrame? How to get the first or last few rows from a Series in Pandas? Find the index position where the minimum and maximum value exist in Pandas DataFrame; Pandas find row where values for column is maximum; Pandas set Index on multiple columns; How to generate demo on a randomly generated DataFrame?


How to get column names in Pandas dataframe, Hashing · Graph · Advanced Data Structure · Matrix · Strings · All Data Structures Let's discuss how to get column names in Pandas dataframe. Now let's try to get the columns name from above dataset. two columns in Pandas DataFrame · Python | Creating a Pandas dataframe column based on a given condition  Let’s see the different ways of changing Data Type for one or more columns in Pandas Dataframe. We can pass any Python, Numpy or Pandas datatype to change all columns of a dataframe to that type, or we can pass a dictionary having column names as keys and datatype as values to change type of selected columns.


Python, Pandas dataframe.select_dtypes() function return a subset of the DataFrame's columns based on the column dtypes. Let's use the dataframe.select_dtypes() function to select all columns having float data type in the dataframe. VS re.​findall() · Convert Python List to numpy Arrays · Convert integer to string in Python​. pandas.DataFrame.select_dtypes¶ DataFrame.select_dtypes (self, include=None, exclude=None) → 'DataFrame' [source] ¶ Return a subset of the DataFrame’s columns based on the column dtypes. Parameters include, exclude scalar or list-like. A selection of dtypes or strings to be included/excluded. At least one of these parameters must be


Get list from Pandas DataFrame column, A step-by-step Python code example that shows how to convert a column in a Pandas DataFrame to a list. Provided by Data Interview Questions, a mailing list  You may use the following syntax to check the data type of all columns in pandas DataFrame: df.dtypes Alternatively, you may use the syntax below to check the data type of a particular column in pandas DataFrame: df['DataFrame Column'].dtypes Steps to Check the Data Type in Pandas DataFrame Step 1: Gather the Data for the DataFrame