xgb分类问题

# This script shows you how to make a submission using a few

# useful Python libraries.

# It gets a public leaderboard score of 0.76077.

# Maybe you can tweak it and do better...?

import pandas as pd

import xgboost as xgb

from sklearn.preprocessing import LabelEncoder

import numpy as np

# Load the data

train_df = pd.read_csv('./input/train.csv', header=0)

test_df = pd.read_csv('./input/test.csv', header=0)

# We'll impute missing values using the median for numeric columns and the most

# common value for string columns.

# This is based on some nice code by 'sveitser' at http://stackoverflow.com/a/25562948

from sklearn.base import TransformerMixin

class DataFrameImputer(TransformerMixin):

    def fit(self, X, y=None):

        self.fill = pd.Series([X[c].value_counts().index[0]

            if X[c].dtype == np.dtype('O') else X[c].median() for c in X],

            index=X.columns)

        return self

    def transform(self, X, y=None):

        return X.fillna(self.fill)

feature_columns_to_use = ['Pclass','Sex','Age','Fare','Parch']

nonnumeric_columns = ['Sex']

# Join the features from train and test together before imputing missing values,

# in case their distribution is slightly different

big_X = train_df[feature_columns_to_use].append(test_df[feature_columns_to_use])

big_X_imputed = DataFrameImputer().fit_transform(big_X)

# XGBoost doesn't (yet) handle categorical features automatically, so we need to change

# them to columns of integer values.

# See http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing for more

# details and options

le = LabelEncoder()

for feature in nonnumeric_columns:

    big_X_imputed[feature] = le.fit_transform(big_X_imputed[feature])

# Prepare the inputs for the model

train_X = big_X_imputed[0:train_df.shape[0]].as_matrix()

test_X = big_X_imputed[train_df.shape[0]:].as_matrix()

train_y = train_df['Survived']

# You can experiment with many other options here, using the same .fit() and .predict()

# methods; see http://scikit-learn.org

# This example uses the current build of XGBoost, from https://github.com/dmlc/xgboost

gbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05).fit(train_X, train_y)

predictions = gbm.predict(test_X)

# Kaggle needs the submission to have a certain format;

# see https://www.kaggle.com/c/titanic-gettingStarted/download/gendermodel.csv

# for an example of what it's supposed to look like.

submission = pd.DataFrame({ 'PassengerId': test_df['PassengerId'],

                            'Survived': predictions })

submission.to_csv("submission.csv", index=False)

替换

df = df.drop('column_name', 1)

where 1 is the axis number (0 for rows and 1 for columns.)

To delete the column without having to reassign df you can do:

df.drop('column_name', axis=1, inplace=True)

Finally, to drop by column number instead of by column label, try this to delete, e.g. the 1st, 2nd and 4th columns:

df.drop(df.columns[[0, 1, 3]], axis=1)  # df.columns is zero-based pd.Index

columns = ['Col1', 'Col2', ...]

df.drop(columns, inplace=True, axis=1)

This will delete one or more columns in-place. Note that inplace=True was added in pandas v0.13 and won't work on older versions, do you'd have to do assign the result back in that case:

df = df.drop(columns, axis=1)