12.1.2: Data Loading and Manipulation (Python)
- Page ID
- 53424
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Data Loading and Manipulation (Python)
Python’s data-centric libraries make it easy to load and manipulate datasets. For example, using pandas one can read a CSV file and perform filtering and grouping:
import pandas as pd
# Load data
df = pd.read_csv("sales_data.csv") # load a CSV file into a DataFrame
print(df.head())
# Filter rows and compute a new column
df_filtered = df[df["region"] == "North"] # filter rows
df_filtered["profit_margin"] = df_filtered["profit"] / df_filtered["revenue"]
# Group and summarize
summary = df_filtered.groupby("product").agg({
"revenue": "sum",
"units_sold": "sum"
}).reset_index()
print(summary)
In this snippet, df = pd.read_csv(...) loads data into a DataFrame. We then filter with Boolean indexing (df[df["region"] == "North"]) and use groupby(...).agg(...) to aggregate by product. Pandas operations are efficient for tables that fit in memory, and they mirror data-frame operations in R.
Key points about pandas/NumPy data manipulation include:
- Labeled data: Pandas DataFrame and Series have row/column labels, making data exploration intuitive.
- Vectorized operations: Many operations (arithmetic, logical, mathematical) are applied column-wise in C for speed, thanks to NumPy under the hood[4].
- Rich API: Methods for handling missing data (fillna, dropna), merging tables (merge), reshaping (pivot, melt), and time series support.