如何使用熊猫从一个数据框中创建测试并训练样本？

我有一个数据框形式的相当大的数据集，我想知道如何将数据框分成两个随机样本（80％和20％）进行训练和测试。

谢谢！

我只会用numpy的randn：

In [11]: df = pd.DataFrame(np.random.randn(100, 2))

In [12]: msk = np.random.rand(len(df)) < 0.8

In [13]: train = df[msk]

In [14]: test = df[~msk]

只是看到它起作用了：

In [15]: len(test)
Out[15]: 21

In [16]: len(train)
Out[16]: 79

scikit Learn'strain_test_split是一个不错的选择。

from sklearn.model_selection import train_test_split

train, test = train_test_split(df, test_size=0.2)

熊猫随机样本也可以

train=df.sample(frac=0.8,random_state=200) #random state is a seed value
test=df.drop(train.index)

我将使用scikit-learn自己的training_test_split，并从索引生成它

from sklearn.model_selection import train_test_split


y = df.pop('output')
X = df

X_train,X_test,y_train,y_test = train_test_split(X.index,y,test_size=0.2)
X.iloc[X_train] # return dataframe train

创建训练/测试甚至验证样本的方法有很多。

情况1：train_test_split没有任何选择的经典方式：

from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.3)

情况2：一个非常小的数据集（小于500行）的情况：为了通过交叉验证获得所有行的结果。最后，您将对可用训练集的每一行都有一个预测。

from sklearn.model_selection import KFold
kf = KFold(n_splits=10, random_state=0)
y_hat_all = []
for train_index, test_index in kf.split(X, y):
    reg = RandomForestRegressor(n_estimators=50, random_state=0)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    clf = reg.fit(X_train, y_train)
    y_hat = clf.predict(X_test)
    y_hat_all.append(y_hat)

情况3a：用于分类目的的不平衡数据集。根据情况1，这是等效的解决方案：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3)

情况3b：用于分类目的的不平衡数据集。在案例2之后，这是等效的解决方案：

from sklearn.model_selection import StratifiedKFold
kf = StratifiedKFold(n_splits=10, random_state=0)
y_hat_all = []
for train_index, test_index in kf.split(X, y):
    reg = RandomForestRegressor(n_estimators=50, random_state=0)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    clf = reg.fit(X_train, y_train)
    y_hat = clf.predict(X_test)
    y_hat_all.append(y_hat)

案例4：您需要在大数据上创建训练/测试/验证集以调整超参数（训练60％，测试20％和验证20％）。

from sklearn.model_selection import train_test_split
X_train, X_test_val, y_train, y_test_val = train_test_split(X, y, test_size=0.6)
X_test, X_val, y_test, y_val = train_test_split(X_test_val, y_test_val, stratify=y, test_size=0.5)

您可以使用以下代码创建测试和训练样本：

from sklearn.model_selection import train_test_split
trainingSet, testSet = train_test_split(df, test_size=0.2)

测试大小可能会有所不同，具体取决于要放入测试和训练数据集中的数据百分比。

有许多有效答案。再增加一堆。从sklearn.cross_validation导入train_test_split

#gets a random 80% of the entire set
X_train = X.sample(frac=0.8, random_state=1)
#gets the left out portion of the dataset
X_test = X.loc[~df_model.index.isin(X_train.index)]

您也可以考虑将训练和测试集分为几部分。初始除法还会随机生成训练和测试集，但要保留原始班级的比例。这使训练和测试集更好地反映了原始数据集的属性。

import numpy as np  

def get_train_test_inds(y,train_proportion=0.7):
    '''Generates indices, making random stratified split into training set and testing sets
    with proportions train_proportion and (1-train_proportion) of initial sample.
    y is any iterable indicating classes of each observation in the sample.
    Initial proportions of classes inside training and 
    testing sets are preserved (stratified sampling).
    '''

    y=np.array(y)
    train_inds = np.zeros(len(y),dtype=bool)
    test_inds = np.zeros(len(y),dtype=bool)
    values = np.unique(y)
    for value in values:
        value_inds = np.nonzero(y==value)[0]
        np.random.shuffle(value_inds)
        n = int(train_proportion*len(value_inds))

        train_inds[value_inds[:n]]=True
        test_inds[value_inds[n:]]=True

    return train_inds,test_inds

df [train_inds]和df [test_inds]为您提供原始DataFrame df的训练和测试集。

如果需要根据数据集中的标签列拆分数据，则可以使用以下方法：

def split_to_train_test(df, label_column, train_frac=0.8):
    train_df, test_df = pd.DataFrame(), pd.DataFrame()
    labels = df[label_column].unique()
    for lbl in labels:
        lbl_df = df[df[label_column] == lbl]
        lbl_train_df = lbl_df.sample(frac=train_frac)
        lbl_test_df = lbl_df.drop(lbl_train_df.index)
        print '\n%s:\n---------\ntotal:%d\ntrain_df:%d\ntest_df:%d' % (lbl, len(lbl_df), len(lbl_train_df), len(lbl_test_df))
        train_df = train_df.append(lbl_train_df)
        test_df = test_df.append(lbl_test_df)

    return train_df, test_df

并使用它：

train, test = split_to_train_test(data, 'class', 0.7)

如果您想控制分割随机性或使用某些全局随机种子，也可以传递random_state。

import pandas as pd

from sklearn.model_selection import train_test_split

datafile_name = 'path_to_data_file'

data = pd.read_csv(datafile_name)

target_attribute = data['column_name']

X_train, X_test, y_train, y_test = train_test_split(data, target_attribute, test_size=0.8)

您可以使用〜（波浪号运算符）排除使用df.sample（）采样的行，让熊猫独自处理采样和索引过滤，以获得两组。

train_df = df.sample(frac=0.8, random_state=100)
test_df = df[~df.index.isin(train_df.index)]

这就是我需要拆分DataFrame时写的内容。我考虑过使用上面的Andy的方法，但不喜欢我无法精确控制数据集的大小（即有时为79，有时为81，等等）。

def make_sets(data_df, test_portion):
    import random as rnd

    tot_ix = range(len(data_df))
    test_ix = sort(rnd.sample(tot_ix, int(test_portion * len(data_df))))
    train_ix = list(set(tot_ix) ^ set(test_ix))

    test_df = data_df.ix[test_ix]
    train_df = data_df.ix[train_ix]

    return train_df, test_df


train_df, test_df = make_sets(data_df, 0.2)
test_df.head()

像这样从df中选择范围行

row_count = df.shape[0]
split_point = int(row_count*1/5)
test_data, train_data = df[:split_point], df[split_point:]

上面有很多不错的答案，因此，如果您想仅使用numpy库为火车和测试集指定确切的样本数量，我想再添加一个示例。

# set the random seed for the reproducibility
np.random.seed(17)

# e.g. number of samples for the training set is 1000
n_train = 1000

# shuffle the indexes
shuffled_indexes = np.arange(len(data_df))
np.random.shuffle(shuffled_indexes)

# use 'n_train' samples for training and the rest for testing
train_ids = shuffled_indexes[:n_train]
test_ids = shuffled_indexes[n_train:]

train_data = data_df.iloc[train_ids]
train_labels = labels_df.iloc[train_ids]

test_data = data_df.iloc[test_ids]
test_labels = data_df.iloc[test_ids]

要将培训分为两个类，例如训练，测试和验证，可以执行以下操作：

probs = np.random.rand(len(df))
training_mask = probs < 0.7
test_mask = (probs>=0.7) & (probs < 0.85)
validatoin_mask = probs >= 0.85


df_training = df[training_mask]
df_test = df[test_mask]
df_validation = df[validatoin_mask]

这将把大约70％的数据用于训练，15％的测试和15％的验证。

您需要将pandas数据帧转换为numpy数组，然后将numpy数组转换回dataframe

 import pandas as pd
df=pd.read_csv('/content/drive/My Drive/snippet.csv', sep='\t')
from sklearn.model_selection import train_test_split

train, test = train_test_split(df, test_size=0.2)
train1=pd.DataFrame(train)
test1=pd.DataFrame(test)
train1.to_csv('/content/drive/My Drive/train.csv',sep="\t",header=None, encoding='utf-8', index = False)
test1.to_csv('/content/drive/My Drive/test.csv',sep="\t",header=None, encoding='utf-8', index = False)

如果您希望有一个数据帧和两个数据帧（不是numpy数组），这应该可以解决问题：

def split_data(df, train_perc = 0.8):

   df['train'] = np.random.rand(len(df)) < train_perc

   train = df[df.train == 1]

   test = df[df.train == 0]

   split_data ={'train': train, 'test': test}

   return split_data

您可以使用df.as_matrix（）函数并创建Numpy数组并将其传递。

Y = df.pop()
X = df.as_matrix()
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2)
model.fit(x_train, y_train)
model.test(x_test)

我觉得更优雅的一点是创建一个随机列，然后将其拆分，这样我们就可以得到一个适合我们需求的拆分并且是随机的。

def split_df(df, p=[0.8, 0.2]):
import numpy as np
df["rand"]=np.random.choice(len(p), len(df), p=p)
r = [df[df["rand"]==val] for val in df["rand"].unique()]
return r

我想如果以后要添加列，还需要获取一个副本而不是数据帧的一部分。

msk = np.random.rand(len(df)) < 0.8
train, test = df[msk].copy(deep = True), df[~msk].copy(deep = True)

这个怎么样？df是我的数据框

total_size=len(df)

train_size=math.floor(0.66*total_size) (2/3 part of my dataset)

#training dataset
train=df.head(train_size)
#test dataset
test=df.tail(len(df) -train_size)

无需转换为numpy。只需使用pandas df进行拆分，它将返回pandas df。

from sklearn.model_selection import train_test_split

train, test = train_test_split(df, test_size=0.2)

shuffle = np.random.permutation(len(df))
test_size = int(len(df) * 0.2)
test_aux = shuffle[:test_size]
train_aux = shuffle[test_size:]
TRAIN_DF =df.iloc[train_aux]
TEST_DF = df.iloc[test_aux]