- Comparison Operators --> greater than (>), less than (<), equal to (==), not equal to (!=), greater than or equal to (>=), less than or equal to (<=)
- Applying comparison operators to filter Numpy array:
arr2 = arr1[arr1 < 20] - Boolean Operators ---> and, or, not
- Boolean Operators for Numpy Arrays ---> logical_and(), logical_or(), logical_not().
Syntax for array elements between 20 and 22:np.logical_and(arr > 20, arr < 22) - Applying Comparison Operators to filter Pandas DataFrames
Syntax:df[df["col"] > 20] - Applying Boolean Operators for Numpy Arrays to filter Pandas DataFrames
Syntax for df with column "col" with values betweem 20 and 22:df[np.logical_and(df["col"] > 20, df["col"] < 22)] - Printing elements in a list with their indices using for loop:
Syntax:for index, elem in enumerate(list):
print("index " + str(index) + ": " + str(elem))
- Dictionary
for key, value in dict.items(): print(key + ":" + str(value)) - Numpy Arrays (especially 2D and above)
for val in np.nditer(my_array): print(val) - Pandas DataFrame
- Printing labels and rows:
for label, row in df.iterrows(): print(label + ": " + row) - Printing specific column from rows:<\br>
for label, row in df.iterrows(): print(label + ": " + row["column") - Adding a new column by applying a function on one of the columns:
df["new_column"] = df["existing_column"].apply(your_transform) - To ensure a variable x doesn't go below a threshold (e.g 10) in a loop:
x = max(10, x-1)