小编Ama*_*ghi的帖子

我有这个数据帧

       X    Y  Z    Value 
0      18   55  1      70   
1      18   55  2      67 
2      18   57  2      75     
3      18   58  1      35  
4      19   54  2      70   

我想将其保存为具有此格式的文本文件

   X    Y  Z    Value 
   18   55  1      70   
   18   55  2      67 
   18   57  2      75     
   18   58  1      35  
   19   54  2      70   

我试过这段代码但是没有用:

np.savetxt('xgboost.txt', a.values, delimiter ='\t')

TypeError: Mismatch between array dtype ('object') and format specifier ('%.18e %.18e %.18e')

我有一个3.5 go的大型csv文件,我想用pandas来阅读它.

这是我的代码:

import pandas as pd
tp = pd.read_csv('train_2011_2012_2013.csv', sep=';', iterator=True, chunksize=20000000, low_memory = False)
df = pd.concat(tp, ignore_index=True)

我收到此错误:

pandas/parser.pyx in pandas.parser.TextReader.read (pandas/parser.c:8771)()

pandas/parser.pyx in pandas.parser.TextReader._read_rows (pandas/parser.c:9731)()

pandas/parser.pyx in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:9602)()

pandas/parser.pyx in pandas.parser.raise_parser_error (pandas/parser.c:23325)()

CParserError: Error tokenizing data. C error: out of 

我的公羊的容量是8 Go.

我必须对一些情绪进行分类,我的数据框是这样的

Phrase                      Sentiment

is it  good movie          positive

wooow is it very goode      positive

bad movie                  negative

我做了一些预处理作为标记化停止词干...等我得到

Phrase                      Sentiment

[ good , movie  ]        positive

[wooow ,is , it ,very, good  ]   positive

[bad , movie ]            negative

我需要最终得到一个数据帧,该行是文本,其值是tf_idf,列是像这样的单词

good     movie   wooow    very      bad                Sentiment

tf idf    tfidf_  tfidf    tf_idf    tf_idf               positive

(其余两条线也一样)

我正在尝试使用基于keras tensorflow的卷积神经网络来训练我的网络，这是我的代码我在函数编译时遇到错误，但我不知道为什么

model = Sequential()  # or Graph or whatever


model.add(Embedding(input_dim = n_symbols + 1,
                    output_dim = vocab_dim,
                    input_length=maxlen,
                    dropout=0.2))

# we add a Convolution1D, which will learn nb_filter
# word group filters of size filter_length:
model.add(Convolution1D(nb_filter=nb_filter,
                        filter_length=filter_length,
                        border_mode='valid',
                        activation='relu',
                        subsample_length=1))
# we use max pooling:
model.add(GlobalMaxPooling1D())

# We add a vanilla hidden layer:
model.add(Dense(hidden_dims))
model.add(Dropout(0.2))
model.add(Activation('relu'))

# We project onto a single unit output layer, and squash it with a sigmoid:
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='mean_squared_error',
              optimizer='adam',
              metrics=['accuracy'])
model.fit(X_train, y_train,
          batch_size=batch_size, …

我有这样的数据帧

     Phrase                          Sentiment

   [ good , movie ]                   positive

   [wooow ,is , it ,very, good  ]   positive

      []                             negative
      []                              pOSTIVE

列短语类型是对象,需要删除包含[]的行,我不知道如何使用python

像这样:

 Phrase                          Sentiment

   [ good , movie ]                   positive

   [wooow ,is , it ,very, good  ]   positive

我的函数返回28个图(图)但我需要将它们分组在一个图上这是我生成28个图的代码

for cat in df.ASS_ASSIGNMENT.unique() :
    a = df.loc[df['ASS_ASSIGNMENT'] == cat]
    dates = a['DATE']
    prediction = a['CSPL_RECEIVED_CALLS']
    plt.plot(dates,prediction)  
    plt.ylabel("nmb_app")
    plt.legend([cat.decode('utf-8')],loc='best')
    plt.xlabel(cat.decode('utf-8'))