This tutorial shows examples of checking if an item is in a Python list. Method 1: item in list_name Method 2: list_name.index(item) Method 3: list_name.count(item) Example for method 1: Check if an item in a list The following code checks if the item of number 6 is in a list. The following is the output, … Read more
This tutorial uses 2 examples to show how to count the number of NaN in Pandas dataframes. Method 1: count the number of NaN by columns: df.isnull().sum() Method 2: count the number of NaN in the whole dataframe: df.isnull().sum().sum() Example for Method 1 The following counts the number of NaN by columns using df.isnull().sum(). The … Read more
There are at least 4 methods to subset row in Pandas dataframes. Method 1: loc[[Comma]] df.loc[[row_number1, row_number_2]] Method 2: loc[Colon] df.loc[row_number1: row_number_2] Method 3: iloc[[Comma]] df.iloc[[row_number1, row_number_2]] Method 4: iloc[Colon] df.iloc[[row_number1: row_number_2]] Example 1 for Method 1 The following uses loc[[Comma]] (i.e., loc[[0,2]])to subset rows in a Pandas dataframe. The following shows the original dataframe … Read more
You can check if any value is NaN in a dataframe in Pandas in Python by using the following 2 methods. Method 1: check if any value is NaN by columns: df.isnull().any() Method 2: Check if any value is NaN in the whole dataframe: df.isnull().any().any() Example for Method 1 The following checks if any value … Read more
You can replace NaN with Blank/Empty cells using either fillna() or replace() in Python. Single Column: Method 1: df[‘Column_name’].fillna(‘ ‘) Method 2: df[‘Column_name’].replace(np.nan,’ ‘, regex=True) Whole dataframe: Method 1: df.fillna(‘ ‘) Method 2: df.replace(np.nan, ‘ ‘, regex=True) Example 1: single column The following uses fillna() to replace NaN with empty cells in a single column. The updated dataframe has … Read more
NumPy Random is from NumPy, whereas Python Random is a module in Python. That is, Python random is NOT part of NumPy. This tutorial uses two examples to show the difference between NumPy Random and Python Random. Example 1 Python Random’s randint only has parameters of the range, whereas NumPy random’s randint has the additional … Read more
random.seed() function can help save the state of random functions. Thus, by using seed(), these random functions can generate the same numbers on multiple code executions. Example 1 Example 1 shows how to use random.seed() and how it impacts the generated numbers. Note that, random.random() generates a floating point number in the range 0.0 <= X < 1.0. The following … Read more
The following is the rule of using ddof in np.std() in Numpy. Rule 1: If you are calculating standard deviation for a sample, set ddof = 1 in np.std(). np.std(sample_name, ddof=1) Rule 2: If you are calculating standard deviation for a population, set ddof = 0 in np.std(). np.std(population_name, ddof=0) Example of ddof = 1 … Read more
This tutorial shows how you can use Numpy to generate random numbers in Python. The following is the basic syntax summarizing 3 functions. 1. Integers: np.random.randint() 2. Normal distribution: np.random.randn() 3. Uniform distribution: np.random.rand() Example 1: Integer np.random.randint(low, high=None, size=None, dtype=int) np.random.randint() will return integer numbers. Given that there are quite a few parameters in randint(), it is … Read more
You can use round() and apply() to round up and down numbers in Pandas. Round to specific decimal places: df.round(decimals = number of specific decimal places) Round up numbers: df[‘DataFrame column’].apply(np.ceil) Method 3: Round down values: df.apply(np.floor) Data being used The following is a column of numbers that we are going to use in this … Read more