Get the name of a pandas DataFrame

You can name the dataframe with the following, and then call the name wherever you like:

import pandas as pd
df = pd.DataFrame( data=np.ones([4,4]) )
df.name = 'Ones'

print df.name
>>>
Ones

Sometimes df.name doesn't work.

you might get an error message:

'DataFrame' object has no attribute 'name'

try the below function:

def get_df_name(df):
    name =[x for x in globals() if globals()[x] is df][0]
    return name

In many situations, a custom attribute attached to a pd.DataFrame object is not necessary. In addition, note that pandas-object attributes may not serialize. So pickling will lose this data.

Instead, consider creating a dictionary with appropriately named keys and access the dataframe via dfs['some_label'].

df = pd.DataFrame()

dfs = {'some_label': df}

DataFrames don't have names, but you have an (experimental) attribute dictionary you can use. For example:

df.attrs['name'] = "My name"   # Can be retrieved later

attributes are retained through some operations.

From here what I understand DataFrames are:

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects.

And Series are:

Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.).

Series have a name attribute which can be accessed like so:

 In [27]: s = pd.Series(np.random.randn(5), name='something')

 In [28]: s
 Out[28]: 
 0    0.541
 1   -1.175
 2    0.129
 3    0.043
 4   -0.429
 Name: something, dtype: float64

 In [29]: s.name
 Out[29]: 'something'

EDIT: Based on OP's comments, I think OP was looking for something like:

 >>> df = pd.DataFrame(...)
 >>> df.name = 'df' # making a custom attribute that DataFrame doesn't intrinsically have
 >>> print(df.name)
 'df'

I am working on a module for feature analysis and I had the same need as yours, as I would like to generate a report with the name of the dataframe being analyzed. To solve this, I used the same solution presented by @scohe001, implemented with the inspect library:

import inspect

def aux_retrieve_name(var):
    callers_local_vars = inspect.currentframe().f_back.f_back.f_locals.items()
    return [var_name for var_name, var_val in callers_local_vars if var_val is var]

Note the additional .f_back term since I intend to call it from another function:

def header_generator(df):
    print('--------- Feature Analyzer ----------')
    print('Dataframe name: "{}"'.format(aux_retrieve_name(df)))
    print('Memory usage: {:03.2f} MB'.format(df.memory_usage(deep=True).sum() / 1024 ** 2))
    return

Running this code with a given dataframe, I get the following output:

header_generator(trial_dataframe)

--------- Feature Analyzer ----------
Dataframe name: "trial_dataframe"
Memory usage: 63.08 MB

Here is a sample function: 'df.name = file` : Sixth line in the code below

def df_list():
    filename_list = current_stage_files(PATH)
    df_list = []
    for file in filename_list:
        df = pd.read_csv(PATH+file)
        df.name = file
        df_list.append(df)
    return df_list

To get the variable name for a DataFrame in Python, you can't do it directly using native Python or pandas attributes. However, you can use a workaround by examining the globals() or locals() dictionaries to match IDs. Here's a function that can find the name of a DataFrame:

def find_df_name(df):
   name = [name for name, obj in globals().items() if id(obj) == id(df)]
   return name[0] if name else None

usage:

df_name = find_df_name(burnishing_defects)
print(df_name)