# Using interpolation to replace values in a dataframe.

When using some machine learning methods, replacing NaN values in a data frame is required. In my previous projects I utilized different methods like dropping the rows or columns with NaN `.dropna()`, or replacing them with zero or `.mean()`.

While working a new project, those methods did not make sense when applied to the data, and I discovered using interpolation, `.interpolate()`, to approximate the missing values.

Here is a simplified example using a data frame that contains NaNs:

```python
Original DataFrame:
      A     B      C        Text
0   1.0  10.0  100.0       apple
1   3.0   NaN  200.0      banana
2   NaN   NaN  300.0      cherry
3   7.0  45.0  400.0        date
4  10.0  50.0    NaN  elderberry
```

## Replacing values in each column

Since the example has text and numeric values, I first separated each so I could apply the interpolation on the numeric columns only.

```python
# Separate numeric and text columns
numeric_columns = df.select_dtypes(include=[np.number])
text_column = df['Text']

# Interpolate numeric columns
numeric_columns_interpolated = numeric_columns.interpolate(method='linear')

# Combine interpolated numeric columns with the text column
df_interpolated = pd.concat([numeric_columns_interpolated, text_column], axis=1)

# Display the DataFrame after interpolation
print("\nDataFrame after interpolation:")
print(df_interpolated)
```

```python
DataFrame after interpolation:
      A          B      C        Text
0   1.0  10.000000  100.0       apple
1   3.0  21.666667  200.0      banana
2   5.0  33.333333  300.0      cherry
3   7.0  45.000000  400.0        date
4  10.0  50.000000  400.0  elderberry
```

## Replacing values across rows

In my project I needed to replace the values across rows. To do this change the `axis=1` parameter in the interpolation line.

```python
# Interpolate numeric columns
numeric_columns_interpolated = numeric_columns.interpolate(method='linear', axis=1)

# Combine interpolated numeric columns with the text column
df_interpolated = pd.concat([numeric_columns_interpolated, text_column], axis=1)

# Display the DataFrame after interpolation
print("\nDataFrame after interpolation:")
print(df_interpolated)
```

```python
DataFrame after interpolation:
      A      B      C        Text
0   1.0   10.0  100.0       apple
1   3.0  101.5  200.0      banana
2   NaN    NaN  300.0      cherry
3   7.0   45.0  400.0        date
4  10.0   50.0   50.0  elderberry
```

Notice there are still NaNs because the first column 'A' has NaN. This requires values in the first column to work. Replace the NaNs in the first column with zeros.

```python
df['A'] = df['A'].fillna(0 ) # Replace the NaN in first column
df
```

```python
DataFrame :
      A     B      C        Text
0   1.0  10.0  100.0       apple
1   3.0   NaN  200.0      banana
2   0.0   NaN  300.0      cherry
3   7.0  45.0  400.0        date
4  10.0  50.0    NaN  elderberry
```

Interpolate again

```python
# Separate numeric and text columns
numeric_columns = df.select_dtypes(include=[np.number])
text_column = df['Text']

# Interpolate numeric columns
numeric_columns_interpolated = numeric_columns.interpolate(method='linear', axis=1)

# Combine interpolated numeric columns with the text column
df_interpolated = pd.concat([numeric_columns_interpolated, text_column], axis=1)

# Display the DataFrame after interpolation
print("\nDataFrame after interpolation:")
print(df_interpolated)
```

```python
DataFrame after interpolation:
      A      B      C        Text
0   1.0   10.0  100.0       apple
1   3.0  101.5  200.0      banana
2   0.0  150.0  300.0      cherry
3   7.0   45.0  400.0        date
4  10.0   50.0   50.0  elderberry
```

Now I have a clean data frame I can use in my project.