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.

Numeric columns have no ,, so converting to strings is not necessary, only use DataFrame.replace with regex=True for substrings replacement:

df = df.replace(',','', regex=True)

Or:

df.replace(',','', regex=True, inplace=True)

And last convert strings columns to numeric, thank you @anki_91:

c = df.select_dtypes(object).columns
df[c] = df[c].apply(pd.to_numeric,errors='coerce')

There is an argument in Pandas DataFrame as pd.read_csv(thousands=',') which is set to None by default.

data = """
State   City    Population Poverty_Rate  Median_Age
VA      XYZ     500,00          10.5%         42
MD      ABC     12,345      8.9%          .
NY      .       987,654     .             41"""

from io import StringIO
import pandas as pd

df = pd.read_csv(StringIO(data),sep='\s+',thousands=',')

print(df)

  State City  Population Poverty_Rate Median_Age
0    VA  XYZ       50000        10.5%         42
1    MD  ABC       12345         8.9%          .
2    NY    .      987654            .         41

Ideally, what you need to do is replace the string markers and then coerce your string columns into integers/floats.

#using your dict.
int_cols = ({"Population": int, "Poverty_Rate": float, "Median_Age": int })

for col in int_cols.keys():
    df[col] = pd.to_numeric(df[col].astype(str).str.replace('%',''),errors='coerce')

print(df.dtypes)

State            object
City             object
Population        int64
Poverty_Rate    float64
Median_Age      float64
dtype: object


print(df)

  State City  Population  Poverty_Rate  Median_Age
0    VA  XYZ       50000          10.5        42.0
1    MD  ABC       12345           8.9         NaN
2    NY    .      987654           NaN        41.0

Pandas has a built in replace method for "object" columns.

df["column"] = df["column"].str.replace(",","").astype(float)

Alternatively check out the pandas.to_numeric() function- I think this should work.

df["column"] = pd.to_numeric(df["column"])

You can also pass arguments for error handling with the pd.to_numeric() function. See the pandas documentation on it.

The pandas read_csv method has a "thousands" optional parameter, which you could use to indicate what this thousands separator is.

Hence, you can use something like

df = pd.read_csv('newdata.csv', sep = ";", thousands = ' ')

to handle your data input.

Notice it will convert your float type to object

df.DollarAmount.apply(lambda x : "{:,}".format(x))
Out[509]: 
0    5,721.48
1     4,000.0
2     4,769.0
3      824.07
4       643.6
5       620.0
Name: DollarAmount, dtype: object