How to Remove Rows with Null Values from a Pandas DataFrame Column?

Dropping Null Values from a Pandas DataFrame Column

To remove rows from a Pandas DataFrame based on null values in a specific column, follow these steps:

1. Identify the Column:
Determine the column(s) in your DataFrame containing the null values you want to remove. In this case, it is the "EPS" column.

2. Use the dropna() Method:
The dropna() method allows you to drop rows based on specific conditions. To drop rows where the "EPS" column is null, use the following syntax:

df = df.dropna(subset=['EPS'])

3. Optional: Specify the Axis (Rows vs. Columns):
By default, dropna() drops rows with null values. If you want to drop columns instead, specify axis=1 as an additional argument:

df = df.dropna(subset=['EPS'], axis=1)

Example:

Consider the DataFrame provided in the question:

df = pd.DataFrame({
    'STK_ID': [601166, 600036, 600016, 601009, 601939, 000001],
    'EPS': [np.nan, np.nan, 4.3, np.nan, 2.5, np.nan],
    'cash': [np.nan, 12, np.nan, np.nan, np.nan, np.nan]
})

Applying the dropna() method results in the following DataFrame:

df.dropna(subset=['EPS'])

   STK_ID  EPS  cash
0  600016   4.3   NaN
1  601939   2.5   NaN

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