# 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)# (1) Round to specific decimal places – Single DataFrame column
df['DataFrame column'].round(decimals=number of decimal places needed)
# (2) Round up – Single DataFrame column
df['DataFrame column'].apply(np.ceil)
# (3) Round down – Single DataFrame column
df['DataFrame column'].apply(np.floor)
# (4) Round to specific decimals places – Entire DataFrame
df.round(decimals=number of decimal places needed)# convert Series
my_series = pd.to_numeric(my_series)
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])
# convert Series
my_series = pd.to_numeric(my_series)
#
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0 8
1 6
2 7.5
3 3
4 0.9
dtype: object
>>> pd.to_numeric(s) # convert everything to float values
0 8.0
1 6.0
2 7.5
3 3.0
4 0.9
dtype: float64