How do u filter data in pandas by columns of different datatype
Question:
I’m having this issue where I got one date table of datetime dtype and another column of float type. Example – DateColumn (year-month-day) and Salary of type float.
How can I take all the rows of lets say 2019 where the salary is higher than 2000.
Answers:
Say you have the data in this way:
date = pd.date_range("2019-01-01", periods=500, freq="D")
salary = np.random.randint(100, 10000, 500)
df = pd.DataFrame({"date": date, "salary": salary})
Then you can just select:
df[(df["date"].dt.year == 2019) & (df["salary"] > 2000)]
If your date column is a string (df["date"] = df["date"].astype(str)), then you can do:
df[(df["date"] >= "2019-01-01") & (df["date"] <= "2019-12-31") & (df["salary"] > 2000)]
I’m having this issue where I got one date table of datetime dtype and another column of float type. Example – DateColumn (year-month-day) and Salary of type float.
How can I take all the rows of lets say 2019 where the salary is higher than 2000.
Say you have the data in this way:
date = pd.date_range("2019-01-01", periods=500, freq="D")
salary = np.random.randint(100, 10000, 500)
df = pd.DataFrame({"date": date, "salary": salary})
Then you can just select:
df[(df["date"].dt.year == 2019) & (df["salary"] > 2000)]
If your date column is a string (df["date"] = df["date"].astype(str)), then you can do:
df[(df["date"] >= "2019-01-01") & (df["date"] <= "2019-12-31") & (df["salary"] > 2000)]