PYTHON

column dataframe to int

df[[column_name]].astype(int)

convert a pandas column to int

# convert Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert  entire Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

convert categorical data type to int in pandas

from sklearn import preprocessing

lab_encoder = preprocessing.LabelEncoder()
df['column'] = lab_encoder.fit_transform(df['column'])

column to int pandas

# convert  entire Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert Series
my_series = pd.to_numeric(my_series)
#
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert Series
my_series = pd.to_numeric(my_series)
#
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert Series
my_series = pd.to_numeric(my_series)
#
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert  entire Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert  entire Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert  entire Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert Series
my_series = pd.to_numeric(my_series)
#
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert  entire Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert Series
my_series = pd.to_numeric(my_series)
#
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert Series
my_series = pd.to_numeric(my_series)
#
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

column to int pandas

# convert  entire Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

how to convert pandas price column to integer

# car_sales is my data, and price is its feature name
# anything that must be replaced is placed in square breackets in repalce
# where square brackets allow us to replace multiple characters
# it will replace thoses characters with empty string
# finally using dtypes, i am changing feature datatype to float
# in my case it is float, u may choose anything u want for example int
car_sales["Price"] = car_sales["Price"].str.replace("[$,]","").astype(float)

how to convert a pandas column price to integer?

car_sales["Price"] = car_sales["Price"].str.replace('[$,]|.d*', '').astype(int)