Alternatives to pandas.DataFrame.from_records for Building DataFrames

Purpose

• pandas.DataFrame.from_records is a function used to create a DataFrame object from various structured data sources:
  • Structured NumPy arrays
  • Sequences of tuples (where each tuple represents a row)
  • Sequences of dictionaries (where each dictionary represents a row)
  • Existing DataFrames (for reshaping or copying)

How it Works

1. Data Input
   You provide the data in one of the supported formats mentioned above.
2. Column Creation
   from_records automatically infers column names from the first element of your data (unless you specify names argument). If using dictionaries, the dictionary keys become column names.
3. Data Population
   Each element in the data sequence becomes a row in the DataFrame. For tuples, elements at corresponding positions map to columns. For dictionaries, dictionary values populate the columns.

Example

import pandas as pd

# Using a list of dictionaries
data = [{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]
df = pd.DataFrame.from_records(data)
print(df)

# Output:
     name  age
0  Alice   30
1    Bob   25

Key Arguments

• coerce_float: Attempt to convert values to numerics (optional)
• exclude: Columns to exclude from the DataFrame (optional)
• index: Field to use as the row index (optional)
• names: A list of column names to use (optional)
• data: The structured data to convert (required)

Benefits

• Efficient for handling structured data.
• Flexible for different data formats.
• Convenient way to create DataFrames from various data structures.

• from_records is a versatile tool for constructing DataFrames in pandas, allowing you to work with data from different sources effectively.
• For complex data structures or nested dictionaries, consider using pandas' higher-level functions like pd.read_csv or pd.read_json for more specialized parsing.

---

Using a list of tuples

import pandas as pd

data = [('Alice', 30), ('Bob', 25), ('Charlie', 42)]
df = pd.DataFrame.from_records(data, columns=['name', 'age'])
print(df)

# Output:
     name  age
0  Alice   30
1    Bob   25
2  Charlie   42

Specifying column names

import pandas as pd

data = [{'name': 'Alice', 'age': 30}, {'city': 'New York', 'age': 25}]
df = pd.DataFrame.from_records(data, columns=['name', 'city', 'age'])  # Order matters
print(df)

# Output (assuming 'city' doesn't exist in the data):
      name  city  age
0  Alice  None   30
1     None  New York   25

Setting the index

import pandas as pd

data = [{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]
df = pd.DataFrame.from_records(data, index='name')
print(df)

# Output:
       age
name
Alice   30
Bob     25

Excluding columns

import pandas as pd

data = [{'name': 'Alice', 'age': 30, 'city': 'New York'}, {'name': 'Bob', 'age': 25}]
df = pd.DataFrame.from_records(data, exclude=['city'])
print(df)

# Output:
     name  age
0  Alice   30
1    Bob   25

import pandas as pd

data = [{'name': 'Alice', 'age': '30'}, {'name': 'Bob', 'age': '25.5'}]
df = pd.DataFrame.from_records(data, coerce_float=True)
print(df.dtypes)

# Output:
# name    object
# age     float64
# dtype: object

---

pandas.DataFrame constructor

• Use the constructor directly if your data is already in a suitable format:
  • List of dictionaries:

    data = [{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]
    df = pd.DataFrame(data)
    

  • List of tuples (assuming consistent column order):

    data = [('Alice', 30), ('Bob', 25), ('Charlie', 42)]
    df = pd.DataFrame(data)
    

pandas.DataFrame.from_dict

• Ideal for dictionaries, especially when you want more control over column order:
  • orient='columns': Keys become columns, values become rows (default).
  • orient='index': Keys become index, values become columns (useful for transposed data).

    data = {'name': ['Alice', 'Bob'], 'age': [30, 25]}
    df = pd.DataFrame.from_dict(data, orient='columns')  # Default behavior
    df_transposed = pd.DataFrame.from_dict(data, orient='index')
    print(df)
    print(df_transposed)
    

pandas.Series concatenation

• Suitable for creating a DataFrame from a single column or multiple Series:

  name_series = pd.Series(['Alice', 'Bob'])
  age_series = pd.Series([30, 25])
  df = pd.DataFrame({'name': name_series, 'age': age_series})
  

• If you need to build a DataFrame from individual Series, use pandas.Series concatenation.
• For more control over column order or using dictionaries, consider pandas.DataFrame.from_dict.
• If your data is a simple list of dictionaries or tuples with consistent structure, pandas.DataFrame.from_records is a good choice.