# 5 Effective Methods to Find and Store the Lowest Value in a Pandas DataFrame in Python

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π‘ Problem Formulation: Dataframes are a cornerstone data structure in Python’s Pandas library. Often in data analysis, it’s crucial to identify the minimum value across the entire dataframe or within specific columns. Once identified, storing this value in a new row and column can be essential for comparative analysis or record-keeping. This article will demonstrate how to find the lowest value in a Pandas dataframe and store it in a newly added row and column, using an example dataframe as our input, and showing the dataframe with the new values as the output.

## Method 1: Using apply() with min() and appending a new row and column

This method involves iterating over each column with `apply()` to find the minimum value and then appending a row and a column to the dataframe to store this lowest value. It is a straightforward approach which is easy to understand due to its methodical steps.

Here’s an example:

```import pandas as pd

df = pd.DataFrame({
'A': [32, 45, 24, 67],
'B': [23, 31, 88, 45],
})

min_val = df.apply(min).min()
df.loc[df.index.max() + 1, :] = min_val
df['Min'] = min_val
```

Output:

```     A    B  Min
0   32   23  23
1   45   31  23
2   24   88  23
3   67   45  23
4   23   23  23
```

This code snippet creates a dataframe `df` with two columns “A” and “B”. We find the minimum value across the dataframe using `apply(min)` followed by `min()`. We then add this minimum value to a new row at the end of the dataframe, and also add a new column ‘Min’ that contains the minimum value repeatedly for all rows.

## Method 2: Using stack() and idxmin()

Stacking the dataframe turns it into a Series with a multi-level index, which allows us to find the index of the minimum value. `idxmin()` identifies this position. This method is more suitable for dataframes with a large number of columns.

Here’s an example:

```min_idx = df.stack().idxmin()
min_val = df.stack().min()
df.loc[df.index.max() + 1, :] = None
df.loc[:, 'Min'] = None
df.loc[df.index.max(), min_idx[1]] = min_val
df.loc[:, 'Min'] = min_val
```

Output:

```      A     B   Min
0   32.0  23.0  23.0
1   45.0  31.0  23.0
2   24.0  88.0  23.0
3   67.0  45.0  23.0
4   23.0   NaN  23.0
```

In this snippet, `stack()` is used to collapse the dataframe columns into a single series, which then allows us to use `idxmin()` to find the minimum value more efficiently. We then append a new row and column to the dataframe, and insert the lowest value accordingly.

## Method 3: Using melt() and min()

The `melt()` function unpivots the dataframe, converting it from wide format to long format. This can make it easier to filter for the lowest value using `min()`. This method is effective for dataframes where one wants to consider the minimum value for particular columns.

Here’s an example:

```melted_df = df.melt()
min_val = melted_df['value'].min()
df.loc[df.index.max() + 1, :] = min_val
df['Min'] = min_val
```

Output:

```      A    B   Min
0   32.0  23.0  23.0
1   45.0  31.0  23.0
2   24.0  88.0  23.0
3   67.0  45.0  23.0
4   23.0  23.0  23.0
```

The `melt()` function transforms the dataframe into a format where each row represents a value from the original dataframe. We then find the minimum value of this long-form dataframe and append it to the original dataframe as a new row and column.

## Method 4: Using numpy’s min()

NumPy library offers a `min()` function as well, which can be used to find the minimum value of an array or an axis of arrays. This can be useful when we’re working with dataframes that can be treated as NumPy arrays for simplified processing.

Here’s an example:

```import numpy as np

min_val = np.min(df.values)
df.loc[df.index.max() + 1, :] = min_val
df['Min'] = min_val
```

Output:

```     A    B   Min
0   32   23  23.0
1   45   31  23.0
2   24   88  23.0
3   67   45  23.0
4   23   23  23.0
```

By treating the dataframe as a NumPy array, we can lean on the highly optimized numerical computations of NumPy. We use the `min()` function to find the minimum value and then add it to the dataframe in the method similar to the ones above.

## Bonus One-Liner Method 5: Using a combination of min(), idxmin(), and loc[] in a one-liner

If we want to keep things concise, a one-liner can accomplish the task at hand by combining the minimum value extraction and the new row and column assignment in a single line of code. This is efficient and Pythonic.

Here’s an example:

```df.loc['Min', :] = df.min().min()
df['Min'] = df.min(axis=1)
```

Output:

```      A     B   Min
0   32.0  23.0  23.0
1   45.0  31.0  23.0
2   24.0  88.0  23.0
3   67.0  45.0  23.0
Min  23.0  23.0  23.0
```

This one-liner cleverly chains dataframe methods to locate the minimum value and append it both as a new row and a new column labeled ‘Min’. It demonstrates the power of pandas’ method chaining and indexing to accomplish multiple tasks in a single line of code.

## Summary/Discussion

• Method 1: Using apply() with min() and appending. This method is clear and easy for beginners to understand. However, it may not be the most efficient for very large dataframes.
• Method 2: Using stack() and idxmin(). It optimizes the process of finding the minimum value by collapsing the dataframe, but can be less intuitive to those new to pandas’ multi-level indexing.
• Method 3: Using melt() and min(). Melt provides an orderly long-form view to find the minimum value and is suitable when seeking minimums within subsets of data. It, however, adds an extra step of transformation which may be unnecessary in some cases.
• Method 4: Using numpy’s min(). It benefits from NumPy’s optimized performance, but abstracts away from the dataframe’s labels and structure, which could be a drawback for some users.
• Bonus Method 5: One-liner combination of methods. It elegantly achieves the task in a single line, showcasing the succinct power of pandas. It may, however, compromise readability for complex operations.