Search results
For selecting only specific columns out of multiple columns for a given value in Pandas: select col_name1, col_name2 from table where column_name = some_value. Options loc: df.loc[df['column_name'] == some_value, [col_name1, col_name2]] or query: df.query('column_name == some_value')[[col_name1, col_name2]]
Try this: Using the setup from @Maxu. col = 'consumption_energy'. conditions = [ df2[col] >= 400, (df2[col] < 400) & (df2[col]> 200), df2[col] <= 200 ] choices = [ "high", 'medium', 'low' ] df2["energy_class"] = np.select(conditions, choices, default=np.nan) consumption_energy energy_class.
26 cze 2024 · How to filter for multiple column values in Pandas? You can filter for multiple column values by using boolean conditions combined with & for AND or | for OR operations.
Oftentimes you’ll want to match certain values with certain columns. Just make values a dict where the key is the column, and the value is a list of items you want to check for.
DataFrame.where(cond, other=nan, *, inplace=False, axis=None, level=None)[source] #. Replace values where the condition is False. Parameters: condbool Series/DataFrame, array-like, or callable. Where cond is True, keep the original value. Where False, replace with corresponding value from other.
To select multiple columns, use a list of column names within the selection brackets []. Note The inner square brackets define a Python list with column names, whereas the outer brackets are used to select the data from a pandas DataFrame as seen in the previous example.
7 sie 2024 · How to Filter DataFrame Based on Multiple Conditions in Pandas. To filter a DataFrame based on multiple conditions, you can use boolean indexing. This involves creating logical conditions (True/False evaluations) for DataFrame columns and combining them using logical operators like & (and), | (or), and ~ (not).
