Power BI vs pandas Comparison Guide for Beginners

import pandas as pd

df = pd.read_csv("sales.csv")
print(df.shape)          # rows, columns
print(df.head())         # first five rows
print(df.dtypes)         # data type of each column
print(df.describe())     # quick statistics

Power BI Desktop - first steps
1. Home > Get Data > Text/CSV > choose sales.csv
2. Click Transform Data to open Power Query
3. Check each column's data type icon
4. Close & Apply to load the table
5. Drag a Card visual and add Sales (Sum)
6. Drag a Bar chart: Region on axis, Sales on values

import pandas as pd

df = pd.read_csv("sales.csv")
df = df.drop_duplicates()
df["order_date"] = pd.to_datetime(df["order_date"])
df["discount"] = df["discount"].fillna(0)
df["region"] = df["region"].str.strip().str.title()
print(df.isna().sum())

let
    Source = Csv.Document(File.Contents("C:\data\sales.csv"), [Delimiter=",", Encoding=65001]),
    Promoted = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),
    Typed = Table.TransformColumnTypes(Promoted, {{"order_date", type date}, {"sales", type number}}),
    NoDupes = Table.Distinct(Typed),
    Trimmed = Table.TransformColumns(NoDupes, {{"region", Text.Trim, type text}})
in
    Trimmed

Total Sales = SUM ( Sales[sales] )

Order Count = COUNTROWS ( Sales )

Avg Order Value = DIVIDE ( [Total Sales], [Order Count] )

North Sales =
CALCULATE ( [Total Sales], Sales[region] = "North" )

summary = (
    df.groupby("region", as_index=False)
      .agg(total_sales=("sales", "sum"),
           orders=("sales", "count"))
)
summary["avg_order_value"] = summary["total_sales"] / summary["orders"]
print(summary.sort_values("total_sales", ascending=False))

Project: Regional sales review
Data: sales.csv (order_id, order_date, region, product, sales, discount)
Questions to answer:
1. What are total sales and order count?
2. Which region sells the most?
3. How did sales change month by month?
4. Which 5 products earn the most?
Deliverables:
- Power BI: one-page dashboard with slicers
- pandas: notebook with tables and two charts

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("sales.csv", parse_dates=["order_date"])
print(df["sales"].sum(), len(df))
print(df.groupby("region")["sales"].sum().sort_values(ascending=False))
monthly = df.groupby(df["order_date"].dt.to_period("M"))["sales"].sum()
monthly.plot(kind="line", title="Monthly sales")
plt.show()
print(df.groupby("product")["sales"].sum().nlargest(5))

1. Load sales.csv and set correct data types
2. Create measures: Total Sales, Order Count
3. Card visuals for both measures
4. Bar chart: Sales by Region
5. Line chart: Sales by order_date (Month)
6. Table: Top 5 products (Top N filter)
7. Add a Region slicer and test interactions
8. Save as sales-review.pbix