Peringkasan data

Analisis Data Eksploratif di Python

Izzy Weber

Curriculum Manager, DataCamp

Mengeksplorasi grup data

.groupby() mengelompokkan data per kategori
Fungsi agregasi menentukan cara merangkum data terkelompok

books[["genre", "rating", "year"]].groupby("genre").mean()

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

Fungsi agregasi

Jumlah: .sum()
Hitung: .count()
Minimum: .min()
Maksimum: .max()
Varians: .var()
Simpangan baku: .std()

Mengagregasi data tak berkelompok

.agg() menerapkan fungsi agregasi pada seluruh DataFrame

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

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

Menentukan agregasi per kolom

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

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

Kolom ringkasan bernama

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 |

Memvisualkan ringkasan kategorikal

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

sebuah barplot genre buku dan rating rata-ratanya

Ayo berlatih!

Analisis Data Eksploratif di Python