Data summarization

Exploratory Data Analysis in Python

Izzy Weber

Curriculum Manager, DataCamp

Exploring groups of data

.groupby() groups data by category
Aggregating function indicates how to summarize grouped data

books.groupby("genre").mean()

|       genre |   rating |        year |
|-------------|----------|-------------|
|   Childrens | 4.780000 | 2015.075000 |
|     Fiction | 4.570229 | 2013.022901 |
| Non Fiction | 4.598324 | 2013.513966 |

Aggregating functions

Sum: .sum()
Count: .count()
Minimum: .min()
Maximum: .max()
Variance: .var()
Standard deviation: .std()

Aggregating ungrouped data

.agg() applies aggregating functions across a DataFrame

books.agg(["mean", "std"])

|      |   rating |        year |
|------|----------|-------------|
| mean | 4.608571 | 2013.508571 |
|  std | 0.226941 |     3.28471 |

Specifying aggregations for columns

books.agg({"rating": ["mean", "std"], "year": ["median"]})

|        |   rating |   year |
|--------|----------|--------|
|   mean | 4.608571 |    NaN |
|    std | 0.226941 |    NaN |
| median |      NaN | 2013.0 |

Named summary columns

books.groupby("genre").agg(
    mean_rating=("rating", "mean"),
    std_rating=("rating", "std"),
    median_year=("year", "median")
)

|  genre      | mean_rating | std_rating | median_year |
|-------------|-------------|------------|-------------|
|   Childrens |    4.780000 |   0.122370 |      2015.0 |
|     Fiction |    4.570229 |   0.281123 |      2013.0 |
| Non Fiction |    4.598324 |   0.179411 |      2013.0 |

Visualizing categorical summaries

sns.barplot(data=books, x="genre", y="rating")
plt.show()

a barplot of book genres and their average ratings

Let's practice!

Exploratory Data Analysis in Python