breakで抜ける、passで何もしないで再度forに戻る
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for x in range((len(own_delated))): if own_delated[x] % 2 == 0: arr = np.append(arr, np.array([[1,own_delated[x]]]), axis=0) break else: pass |
‘int’ object is not iterableが出た場合forでin len()を設定すると
オブジェクトの数値が対象となってしまうのでin range()を使用する
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for i in range((len(a))): |
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for dataset in full_data: dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\ 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare') dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss') dataset['Title'] = dataset['Title'].replace('Ms', 'Miss') dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs') print (train[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()) |
インポート用コード
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pd.read_csv(filename) # From a CSV file pd.read_table(filename) # From a delimited text file (like TSV) pd.read_excel(filename) # From an Excel file pd.read_sql(query, connection_object) # Reads from a SQL table/database pd.read_json(json_string) # Reads from a JSON formatted string, URL or file. pd.read_html(url) # Parses an html URL, string or file and extracts tables to a list of dataframes pd.read_clipboard() # Takes the contents of your clipboard and passes it to read_table() pd.DataFrame(dict) # From a dict, keys for columns names, values for data as lists |
タイタニックのデータmaleを1、femaleを0に変換し、
搭乗地域S,C,Qを0,1,2に変換
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#カテゴリ変数の変換 df['Sex'] = df['Sex'].apply(lambda x: 1 if x == 'male' else 0) df['Embarked'] = df['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int) |
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import numpy as np import pandas as pd import sklearn from sklearn.datasets import load_iris iris_dataset = load_iris() print(iris_dataset.keys()) #>>> ['DESCR', 'target_names', 'feature_names', 'target', 'data'] #DESCRはデータセットの解説があるという事 #その他はデータの種類を示している print(iris_dataset["DESCR"[:193]]+"\n...") #データセットの詳細を知る為にDESCRを表示 |
結果
150のデータと4つのデータの種類設定コード
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Iris Plants Database ==================== Notes ----- Data Set Characteristics: :Number of Instances: 150 (50 in each of three classes) :Number of Attributes: 4 numeric, predictive attributes and the class :Attribute Information: - sepal length in cm - sepal width in cm - petal length in cm - petal width in cm - class: - Iris-Setosa - Iris-Versicolour - Iris-Virginica :Summary Statistics: ============== ==== ==== ======= ===== ==================== Min Max Mean SD Class Correlation ============== ==== ==== ======= ===== ==================== sepal length: 4.3 7.9 5.84 0.83 0.7826 sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) ============== ==== ==== ======= ===== ==================== :Missing Attribute Values: None :Class Distribution: 33.3% for each of 3 classes. :Creator: R.A. Fisher :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) :Date: July, 1988 This is a copy of UCI ML iris datasets. http://archive.ics.uci.edu/ml/datasets/Iris The famous Iris database, first used by Sir R.A Fisher This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. References ---------- - Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950). - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71. - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433. - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II conceptual clustering system finds 3 classes in the data. - Many, many more ... ... |
targetnameの種類を知りたい場合（アイリスの名称一覧）
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print(iris_dataset["target_names"]) |
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['setosa' 'versicolor' 'virginica'] |