how to pass an empty pandas query

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For improvement in a model I am passing several .query() to a pandas dataframe. In the for loop I would to have an empty query as well, but didnt find anything in documentation. It should return the full dataframe.

I tried:


But this doesnt work. Other ideas?


Best I could come up ATM, yet still not satisfactory:

col = temp_df.columns[0]
temp_df.query(f"{col}.isnull() or {col}.notnull()")

pandas.DataFrame.empty — pandas 1.1.1 documentation, Return series without null values. DataFrame.dropna. Return DataFrame with labels on given axis omitted where (all or any) data are missing. Pandas – Check if DataFrame is Empty You can check if a Pandas DataFrame is empty or not. To check if DataFrame is empty in Pandas, use DataFrame. empty. DataFrame. empty returns a boolean indicator if the DataFrame is empty or not.

Use a boolean array that's the same length as your DataFrame:

In [2]: df = pd.DataFrame(np.arange(16).reshape(4, 4), columns=list('ABCD'))

In [3]: trivially_true = np.repeat(True, len(df))

In [4]: trivially_false = np.repeat(False, len(df))

In [5]: df
    A   B   C   D
0   0   1   2   3
1   4   5   6   7
2   8   9  10  11
3  12  13  14  15

In [6]: df.query('@trivially_true')
    A   B   C   D
0   0   1   2   3
1   4   5   6   7
2   8   9  10  11
3  12  13  14  15

In [7]: df.query('@trivially_false')
Empty DataFrame
Columns: [A, B, C, D]
Index: []

pandas.DataFrame.query — pandas 1.1.1 documentation, Query the columns of a DataFrame with a boolean expression. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a� If we only have NaNs in our DataFrame, it is not considered empty! We will need to drop the NaNs to make the DataFrame empty: >>> df = pd.DataFrame( {'A' : [np.nan]}) >>> df A 0 NaN >>> df.empty False >>> df.dropna().empty True. pandas.DataFrame.dtypes pandas.DataFrame.iat.

You can use the 'index' keyword for this:

temp_df.query('index == index or index != index')

This will get you both null & non-null indices of the dataframe i.e. the entire dataframe.

pandas.DataFrame.notnull — pandas 1.1.1 documentation, Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas� The idea is to give query the option to return the full dataframe, via some kind of "emtpy query". I need this functionality quite often, since I am iterating on different querys.

How to Check if Pandas DataFrame is Empty?, To check if DataFrame is empty in Pandas, use DataFrame.empty attribute. The attribute returns a boolean indicator if the DataFrame is empty or not. Examples� Pandas is one of those packages that makes importing and analyzing data much easier. Analyzing data requires a lot of filtering operations. Pandas provide many methods to filter a Data frame and Dataframe.query() is one of them. Syntax: DataFrame.query(expr, inplace=False, **kwargs) Parameters: expr: Expression in string form to filter data.

Pandas DataFrame: empty() function, Returns: bool, If DataFrame is empty, return True, if not return False. Notes. If DataFrame contains only NaNs, it is still not considered empty. pandas.DataFrame.query¶ DataFrame.query (expr, inplace = False, ** kwargs) [source] ¶ Query the columns of a DataFrame with a boolean expression. Parameters expr str. The query string to evaluate. You can refer to variables in the environment by prefixing them with an ‘@’ character like @a + b.

df2 = pd.read_csv('my.csv') query=df2.query('cc_vehicle_line==variable_name') It throws the message that variable_name is undefined.But it is defined. I cannot use hardcoded value as I need to automate and depending of value of variable_name, select relevant rows.

  • What should an empty query return?
  • the full dataframe
  • Why don't you just return temp_df in that case?
  • +1 on this question. A good scenario I routinely have is that i programmatically create queries as in q = some_func(); view = temp_df.query(q) and I'd like to have the "empty" query to return the full dataframe. A trivial solution would be to use something such as if q is None: view = temp_df else view = temp_df.query(q). But I'd rather have not to. An empty query would be ideal for these cases.
  • Another way of putting this is to think of it as the equivalent of WHERE 1 = 1 in SQL.
  • it works :) I guess a more handy solution should me implemented by pandas developers.