PYTHON

column dataframe to int

df[[column_name]].astype(int)

convert column to numeric pandas

# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame

# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)

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"])

python dataframe column string to integer python

my_series = pd.to_numeric(my_series)

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

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"])

convert a column to int pandas

df[column_name]= pd.to_numeric(df[column_name],errors='coerce')

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)

pandas convert column to nullable integers dtype

df['myCol'] = df['myCol'].astype('Int64')

how to convert a pandas column price to integer?

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