How to take a list and fill with values and put into a dataframe

ghz 14hours ago ⋅ 11 views

How to take a list and fill with values and put into a dataframe and filter in Python?

To take a list, fill it with values, and put it into a DataFrame in Python, and then filter the DataFrame, you can use the pandas library. Here's a step-by-step guide with code examples:

1. Create a List of Values

You start by creating a list of values, which could be a list of dictionaries, lists, or individual values depending on your structure.

# Example: A list of lists (rows)
data = [
    [1, 'apple', 10],
    [2, 'banana', 5],
    [3, 'cherry', 15],
    [4, 'date', 7],
    [5, 'elderberry', 20]
]

2. Convert List to DataFrame

Once you have your list, you can convert it into a pandas DataFrame.

import pandas as pd

# Create DataFrame with column names
df = pd.DataFrame(data, columns=['ID', 'Fruit', 'Quantity'])

# Display the DataFrame
print(df)

This will produce the following DataFrame:

   ID       Fruit  Quantity
0   1       apple        10
1   2      banana         5
2   3      cherry        15
3   4        date         7
4   5  elderberry        20

3. Filter the DataFrame

Once the DataFrame is created, you can apply filtering to get specific rows based on conditions.

Example 1: Filter Rows Based on a Condition

Let's say you want to filter rows where the Quantity is greater than 10.

filtered_df = df[df['Quantity'] > 10]

print(filtered_df)

Output:

   ID       Fruit  Quantity
2   3      cherry        15
4   5  elderberry        20

Example 2: Filter Rows Based on Multiple Conditions

You can combine multiple conditions. For example, filter rows where the Quantity is greater than 5 and the Fruit is not 'banana':

filtered_df = df[(df['Quantity'] > 5) & (df['Fruit'] != 'banana')]

print(filtered_df)

Output:

   ID       Fruit  Quantity
0   1       apple        10
2   3      cherry        15
3   4        date         7
4   5  elderberry        20

4. Fill the List with Values

If you want to fill the list with values before putting it into a DataFrame, you can do it dynamically. For example:

# Create a list with some initial values
data = []

# Fill the list with values
for i in range(5):
    data.append([i+1, f'fruit_{i+1}', (i+1)*5])

# Convert the list into a DataFrame
df = pd.DataFrame(data, columns=['ID', 'Fruit', 'Quantity'])

print(df)

Output:

   ID       Fruit  Quantity
0   1     fruit_1         5
1   2     fruit_2        10
2   3     fruit_3        15
3   4     fruit_4        20
4   5     fruit_5        25

5. Filter with .query() (Alternative Approach)

Another elegant way to filter a DataFrame is using the .query() method.

# Example: Filter rows where Quantity > 10
filtered_df = df.query('Quantity > 10')

print(filtered_df)

This will produce the same result as using boolean indexing.

Summary of Key Steps:

  1. Create a list: Use a list of values (e.g., a list of lists or a list of dictionaries).
  2. Convert to DataFrame: Use pd.DataFrame() to create a DataFrame from the list.
  3. Filter: Use boolean indexing or .query() to filter the DataFrame based on conditions.

Let me know if you'd like more details on any specific part!

#py