Pandas and Data Cleaning: A Deeper Dive into Handling Missing Values

As data analysts and scientists, we often encounter datasets that contain missing values. These values can arise due to various reasons such as errors in data entry, missing observations, or simply due to the nature of the data itself. In this article, we will explore how to handle missing values in pandas, a powerful library used for data manipulation and analysis.

Understanding Missing Values

In pandas, missing values are represented by NaN (Not a Number). When working with datasets that contain missing values, it’s essential to understand the type of missing value representation being used. There are several types of missing values, including:

• Missing completely at random (MCAR): The missing values are randomly distributed and do not affect the relationships between the variables.
• Missing at random (MAR): The missing values depend on observed values but not on unobserved values.
• Missing not at random (MNAR): The missing values depend on both observed and unobserved values.

Cleaning Up Data

Before we can start analyzing our data, we need to clean it up. This involves converting the data types of certain columns to numeric, filling any null values as required, and handling missing values appropriately.

Converting Data Types

In the given example, we convert the data types of ‘Apple’, ‘Orange’, and ‘Plump’ columns to numeric using the pd.to_numeric function. The second argument to this function defines how error values are handled.

for col in ['Apple', 'Orange', 'Plump']:
    df[col] = pd.to_numeric(df[col], 'coerce')

In this case, we’re using 'coerce', which replaces any non-numeric value with NaN.

Filling Null Values

We don’t require filling null values in this example, but it’s essential to know how to do so. You can fill missing values with a specific value or use interpolation techniques.

df['Country'] = df['Country'].fillna('Unknown')

In this example, we’re filling the ‘Country’ column with ‘Unknown’ if any value is missing.

Handling Missing Values

Now that our data is cleaned up, let’s focus on handling missing values. We’ll define a function myfunc that takes a row as input and returns a tuple containing two additional columns: 'fruitmean' and 'fruitdiff'.

def myfunc(x):
    vals = pd.Series([x.Apple, x.Orange, x.Plump])
    valfilled = vals.fillna(0)
    nulls = vals.isnull().sum()
    fruitmean = vals.mean() if nulls == 0 else np.nan
    fruitdiff = valfilled.max() - valfilled.min() if nulls < len(vals) else np.nan
    return pd.Series([fruitmean, fruitdiff])

Applying the Function

We can apply this function to each row of our dataframe using the df.apply method.

df[['fruitmean', 'fruitdiff']] = df.apply(myfunc, axis=1)

This will create two new columns in our dataframe: 'fruitmean' and 'fruitdiff', containing the mean value and maximum difference between values for each row respectively.

Exploring Additional Columns

Now that we have our additional columns, let’s explore them. We can use various pandas functions to analyze these columns further.

print(df['fruitmean'].describe())

This will print a summary of the ‘fruitmean’ column, including its count, mean, standard deviation, min value, and max value.

We can also plot a histogram or scatterplot to visualize the distribution of values in this column.

import matplotlib.pyplot as plt

plt.hist(df['fruitmean'], bins=10)
plt.title('Distribution of Fruit Mean')
plt.xlabel('Fruit Mean')
plt.ylabel('Frequency')
plt.show()

Similarly, we can plot a histogram or scatterplot to visualize the distribution of values in the ‘fruitdiff’ column.

import matplotlib.pyplot as plt

plt.hist(df['fruitdiff'], bins=10)
plt.title('Distribution of Fruit Difference')
plt.xlabel('Fruit Difference')
plt.ylabel('Frequency')
plt.show()

Conclusion

In this article, we explored how to handle missing values in pandas using the pd.to_numeric function and defined a custom function myfunc to create additional columns based on specific conditions. We applied this function to each row of our dataframe and analyzed the resulting columns further. Understanding how to handle missing values is an essential skill for any data analyst or scientist, and with the techniques discussed in this article, you’ll be well-equipped to tackle such challenges.

Additional Examples

Let’s consider a few more examples that demonstrate different ways to apply the myfunc function.

Example 1: Handling Missing Values in a Different Way

Instead of using the 'coerce' argument in pd.to_numeric, we can use the errors='raise' argument to raise an error when encountering non-numeric values.

for col in ['Apple', 'Orange', 'Plump']:
    df[col] = pd.to_numeric(df[col], errors='raise')

This will raise a ValueError if any non-numeric value is encountered.

Example 2: Handling Missing Values Using Interpolation

We can use the interpolate function from pandas to fill missing values in a row-wise manner.

df[['fruitmean', 'fruitdiff']] = df.apply(lambda x: pd.Series([x.Apple.mean(), x.Orange.mean()]), axis=1)

This will calculate the mean value for each column and return it as the corresponding element in the tuple returned by myfunc.

Example 3: Handling Missing Values Using GroupBy

We can use the groupby function to group rows based on certain conditions and calculate the desired values.

df_grouped = df.groupby(['Country', 'Year']).agg({'Apple': 'mean', 'Orange': 'mean', 'Plump': 'mean'})

def myfunc(x):
    vals = pd.Series([x['Apple'], x['Orange'], x['Plump']])
    valfilled = vals.fillna(0)
    nulls = vals.isnull().sum()
    fruitmean = vals.mean() if nulls == 0 else np.nan
    fruitdiff = valfilled.max() - valfilled.min() if nulls < len(vals) else np.nan
    return pd.Series([fruitmean, fruitdiff])

This will group rows by country and year, calculate the mean value for each column, fill missing values, and calculate the maximum difference.

By applying these techniques, you’ll be able to handle missing values effectively in your pandas dataframes.

Last modified on 2023-09-25