Αποτελέσματα Αναζήτησης
31 Αυγ 2022 · You could do df.drop('col1', axis=1).drop('col2', axis=1)..... Or better drop all cols in one operation, and possibly inplace with df.drop(['col1','col2','col5','colN'], axis=1, inplace=True) – zyxue
11 Μαΐ 2023 · The pandasql Python library allows querying pandas dataframes by running SQL commands without having to connect to any SQL server. Under the hood, it uses SQLite syntax , automatically detects any pandas dataframe, and treats it as a regular SQL table.
18 Ιουν 2017 · To select a column or columns you can use the following: df['A'] or df.loc[:,'A'] or. df[['A','B']] or df.loc[:,['A','B']] To use the .query method you do something like. df.query('A > B') which would return all the rows where the value in column A is greater than the value in column b.
26 Ιουν 2024 · Let’s discuss how to drop one or multiple columns in Pandas Dataframe. To Delete a column from a Pandas DataFrame or Drop one or multiple columns in a Pandas Dataframe can be achieved in multiple ways. # Drop column 'B' df = df.drop('B', axis=1) # Drop columns multiple column df = df.drop(['B', 'C'], axis=1)
24 Ιαν 2023 · You can use the following methods to drop multiple columns from a pandas DataFrame: Method 1: Drop Multiple Columns by Name. df.drop(columns=['col1', 'col2', 'col4'], inplace=True) Method 2: Drop Columns in Range by Name. df.drop(columns=df.loc[:, 'col1':'col4'], inplace=True) Method 3: Drop Multiple Columns by Index.
24 Αυγ 2020 · Let’s see how you can use the .drop() method to drop multiple columns by name, by dropping the 'Current' and 'Location' columns: # Drop Multiple Pandas Columns By Name import pandas as pd df = pd.DataFrame({ 'Name': ['Nik', 'Kate', 'Evan', 'Kyra'], 'Age': [33, 32, 36, None], 'Location': ['Canada', 'USA', None, None], 'Current': [True, False ...
5 Ιουλ 2018 · To reference external variables in the query, use @variable_name: import pandas as pd import numpy as np df = pd.DataFrame({ 'name':['john','david','anna'], 'country':['USA','UK',np.nan], 'age':[23,45,45] }) target_age = 45 df.query('age == @target_age')
