remove columns that start with pandas

del df['column']

Here is what the above code is Doing:
1. We’re creating a new DataFrame called df_subset that contains only the columns we want.
2. We’re using the .drop() method to remove the column we don’t want from the original DataFrame.

The first method is the preferred way to subset your data. It’s more explicit and it’s easier to read.

The second method is useful if you want to remove multiple columns at once.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.

Let’s say we want to remove the ‘age’ and ‘education’ columns from our DataFrame. We can do this by passing a list of column names to the .drop() method:

df = df.drop([‘age’, ‘education’], axis=1)

The axis=1 argument tells the .drop() method that we want to remove columns, not rows.