["python","pandas","select","filter","conditional-statements"]
Selecting with complex criteria from pandas.DataFrame

For example I have simple DF: import pandas as pd from random import randint df = pd.DataFrame({'A': [randint(1, 9) for x in range(10)], 'B': [randint(1, 9)*10 for x in range(10)], 'C': [randint(1, 9)*100 for x in range(10)]}) Can I select values from 'A' for which corresponding values for 'B' will be greater than 50, and for 'C' - not equal to 900, using methods and idioms of Pandas?
Sure! Setup: >>> import pandas as pd >>> from random import randint >>> df = pd.DataFrame({'A': [randint(1, 9) for x in range(10)], 'B': [randint(1, 9)*10 for x in range(10)], 'C': [randint(1, 9)*100 for x in range(10)]}) >>> df A B C 0 9 40 300 1 9 70 700 2 5 70 900 3 8 80 900 4 7 50 200 5 9 30 900 6 2 80 700 7 2 80 400 8 5 80 300 9 7 70 800 We can apply column operations and get boolean Series objects: >>> df["B"] > 50 0 False 1 True 2 True 3 True 4 False 5 False 6 True 7 True 8 True 9 True Name: B >>> (df["B"] > 50) & (df["C"] != 900) or >>> (df["B"] > 50) & ~(df["C"] == 900) 0 False 1 False 2 True 3 True 4 False 5 False 6 False 7 False 8 False 9 False [Update, to switch to new-style .loc]: And then we can use these to index into the object. For read access, you can chain indices: >>> df["A"][(df["B"] > 50) & (df["C"] != 900)] 2 5 3 8 Name: A, dtype: int64 but you can get yourself into trouble because of the difference between a view and a copy doing this for write access. You can use .loc instead: >>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"] 2 5 3 8 Name: A, dtype: int64 >>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"].values array([5, 8], dtype=int64) >>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"] *= 1000 >>> df A B C 0 9 40 300 1 9 70 700 2 5000 70 900 3 8000 80 900 4 7 50 200 5 9 30 900 6 2 80 700 7 2 80 400 8 5 80 300 9 7 70 800