Teks menjadi DataFrame

Sekarang setelah Anda membuat fitur berbasis hitungan dalam sebuah array, Anda perlu memformat ulang agar dapat digabungkan dengan himpunan data lainnya. Ini dapat dilakukan dengan mengonversi array tersebut menjadi pandas DataFrame, dengan nama fitur yang Anda temukan sebelumnya sebagai nama kolom, lalu menggabungkannya dengan DataFrame asli.

Numpy array (cv_array) dan vectorizer (cv) yang Anda fit pada latihan sebelumnya tersedia di workspace Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur untuk Machine Learning di Python

Petunjuk latihan

Buat DataFrame cv_df yang berisi cv_array sebagai nilai dan nama fitur sebagai nama kolom.
Tambahkan prefiks Counts_ pada nama kolom agar mudah diidentifikasi.
Gabungkan DataFrame ini (cv_df) dengan DataFrame asli (speech_df) secara kolom (column-wise).

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a DataFrame with these features
cv_df = pd.DataFrame(____, 
                     columns=____).____('Counts_')

# Add the new columns to the original DataFrame
speech_df_new = ____([speech_df, cv_df], axis=1, sort=False)
print(speech_df_new.head())

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Rekayasa Fitur untuk Machine Learning di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns.

Exercise 1: Why generate features?Exercise 2: Getting to know your data Exercise 3: Selecting specific data types Exercise 4: Dealing with categorical features Exercise 5: One-hot encoding and dummy variables Exercise 6: Dealing with uncommon categories Exercise 7: Numeric variables Exercise 8: Binarizing columns Exercise 9: Binning values

This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.

Exercise 1: Why do missing values exist?Exercise 2: How sparse is my data?Exercise 3: Finding the missing values Exercise 4: Dealing with missing values (I)Exercise 5: Listwise deletion Exercise 6: Replacing missing values with constants Exercise 7: Dealing with missing values (II)Exercise 8: Filling continuous missing values Exercise 9: Imputing values in predictive models Exercise 10: Dealing with other data issues Exercise 11: Dealing with stray characters (I)Exercise 12: Dealing with stray characters (II)Exercise 13: Method chaining

In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.

Exercise 1: Data distributions Exercise 2: What does your data look like? (I)Exercise 3: What does your data look like? (II)Exercise 4: When don't you have to transform your data?Exercise 5: Scaling and transformations Exercise 6: Normalization Exercise 7: Standardization Exercise 8: Log transformation Exercise 9: When can you use normalization?Exercise 10: Removing outliers Exercise 11: Percentage based outlier removal Exercise 12: Statistical outlier removal Exercise 13: Scaling and transforming new data Exercise 14: Train and testing transformations (I)Exercise 15: Train and testing transformations (II)

Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.

Exercise 1: Pengodean teks Exercise 2: Membersihkan teks Anda Exercise 3: Fitur teks tingkat tinggi Exercise 4: Jumlah kata Exercise 5: Menghitung kata (I)Exercise 6: Menghitung kata (II)Exercise 7: Membatasi fitur Anda Exercise 8: Teks menjadi DataFrame

Latihan Saat Ini

Exercise 9: Frekuensi istilah–invers frekuensi dokumen Exercise 10: Tf-idf Exercise 11: Memeriksa nilai Tf-idf Exercise 12: Mentransformasi data yang belum pernah dilihat Exercise 13: N-gram Exercise 14: Menggunakan n-gram yang lebih panjang Exercise 15: Menemukan kata yang paling umum Exercise 16: Ringkasan