Trying to remove commas and dollars signs with Pandas in Python

You have to access the str attribute per http://pandas.pydata.org/pandas-docs/stable/text.html

df1['Avg_Annual'] = df1['Avg_Annual'].str.replace(',', '')
df1['Avg_Annual'] = df1['Avg_Annual'].str.replace('$', '')
df1['Avg_Annual'] = df1['Avg_Annual'].astype(int)

alternately;

df1['Avg_Annual'] = df1['Avg_Annual'].str.replace(',', '').str.replace('$', '').astype(int)

if you want to prioritize time spent typing over readability.

Shamelessly stolen from this answer... but, that answer is only about changing one character and doesn't complete the coolness: since it takes a dictionary, you can replace any number of characters at once, as well as in any number of columns.

# if you want to operate on multiple columns, put them in a list like so:
cols = ['col1', 'col2', ..., 'colN']

# pass them to df.replace(), specifying each char and it's replacement:
df[cols] = df[cols].replace({'\$': '', ',': ''}, regex=True)

@shivsn caught that you need to use regex=True; you already knew about replace (but also didn't show trying to use it on multiple columns or both the dollar sign and comma simultaneously).

This answer is simply spelling out the details I found from others in one place for those like me (e.g. noobs to python an pandas). Hope it's helpful.

@bernie's answer is spot on for your problem. Here's my take on the general problem of loading numerical data in pandas.

Often the source of the data is reports generated for direct consumption. Hence the presence of extra formatting like %, thousand's separator, currency symbols etc. All of these are useful for reading but causes problems for the default parser. My solution is to typecast the column to string, replace these symbols one by one then cast it back to appropriate numerical formats. Having a boilerplate function which retains only [0-9.] is tempting but causes problems where the thousand's separator and decimal gets swapped, also in case of scientific notation. Here's my code which I wrap into a function and apply as needed.

df[col] = df[col].astype(str)  # cast to string

# all the string surgery goes in here
df[col] = df[col].replace('$', '')
df[col] = df[col].replace(',', '')  # assuming ',' is the thousand's separator in your locale
df[col] = df[col].replace('%', '')

df[col] = df[col].astype(float)  # cast back to appropriate type