[英]AttributeError: module 'pandas' has no attribute 'to_csv'
I took some rows from csv file like this我从这样的 csv 文件中取出了一些行
pd.DataFrame(CV_data.take(5), columns=CV_data.columns)
and performed some functions on it.并对其执行了一些功能。 now i want to save it in csv again but it is giving error
module 'pandas' has no attribute 'to_csv'
I am trying to save it like this现在我想再次将它保存在 csv 中,但它给出了错误
module 'pandas' has no attribute 'to_csv'
我正在尝试像这样保存它
pd.to_csv(CV_data, sep='\t', encoding='utf-8')
here is my full code.这是我的完整代码。 how can i save my resulting data in csv or excel?
如何将结果数据保存在 csv 或 excel 中?
# Disable warnings, set Matplotlib inline plotting and load Pandas package
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import pandas as pd
pd.options.display.mpl_style = 'default'
CV_data = sqlContext.read.load('Downloads/data/churn-bigml-80.csv',
format='com.databricks.spark.csv',
header='true',
inferSchema='true')
final_test_data = sqlContext.read.load('Downloads/data/churn-bigml-20.csv',
format='com.databricks.spark.csv',
header='true',
inferSchema='true')
CV_data.cache()
CV_data.printSchema()
pd.DataFrame(CV_data.take(5), columns=CV_data.columns)
from pyspark.sql.types import DoubleType
from pyspark.sql.functions import UserDefinedFunction
binary_map = {'Yes':1.0, 'No':0.0, True:1.0, False:0.0}
toNum = UserDefinedFunction(lambda k: binary_map[k], DoubleType())
CV_data = CV_data.drop('State').drop('Area code') \
.drop('Total day charge').drop('Total eve charge') \
.drop('Total night charge').drop('Total intl charge') \
.withColumn('Churn', toNum(CV_data['Churn'])) \
.withColumn('International plan', toNum(CV_data['International plan'])) \
.withColumn('Voice mail plan', toNum(CV_data['Voice mail plan'])).cache()
final_test_data = final_test_data.drop('State').drop('Area code') \
.drop('Total day charge').drop('Total eve charge') \
.drop('Total night charge').drop('Total intl charge') \
.withColumn('Churn', toNum(final_test_data['Churn'])) \
.withColumn('International plan', toNum(final_test_data['International plan'])) \
.withColumn('Voice mail plan', toNum(final_test_data['Voice mail plan'])).cache()
pd.DataFrame(CV_data.take(5), columns=CV_data.columns)
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.tree import DecisionTree
def labelData(data):
# label: row[end], features: row[0:end-1]
return data.map(lambda row: LabeledPoint(row[-1], row[:-1]))
training_data, testing_data = labelData(CV_data).randomSplit([0.8, 0.2])
model = DecisionTree.trainClassifier(training_data, numClasses=2, maxDepth=2,
categoricalFeaturesInfo={1:2, 2:2},
impurity='gini', maxBins=32)
print (model.toDebugString())
print ('Feature 12:', CV_data.columns[12])
print ('Feature 4: ', CV_data.columns[4] )
from pyspark.mllib.evaluation import MulticlassMetrics
def getPredictionsLabels(model, test_data):
predictions = model.predict(test_data.map(lambda r: r.features))
return predictions.zip(test_data.map(lambda r: r.label))
def printMetrics(predictions_and_labels):
metrics = MulticlassMetrics(predictions_and_labels)
print ('Precision of True ', metrics.precision(1))
print ('Precision of False', metrics.precision(0))
print ('Recall of True ', metrics.recall(1))
print ('Recall of False ', metrics.recall(0))
print ('F-1 Score ', metrics.fMeasure())
print ('Confusion Matrix\n', metrics.confusionMatrix().toArray())
predictions_and_labels = getPredictionsLabels(model, testing_data)
printMetrics(predictions_and_labels)
CV_data.groupby('Churn').count().toPandas()
stratified_CV_data = CV_data.sampleBy('Churn', fractions={0: 388./2278, 1: 1.0}).cache()
stratified_CV_data.groupby('Churn').count().toPandas()
pd.to_csv(CV_data, sep='\t', encoding='utf-8')
to_csv
is a method of a DataFrame
object, not of the pandas
module. to_csv
是DataFrame
对象的方法,而不是pandas
模块的方法。
df = pd.DataFrame(CV_data.take(5), columns=CV_data.columns)
# whatever manipulations on df
df.to_csv(...)
You also have a line pd.DataFrame(CV_data.take(5), columns=CV_data.columns)
in your code.您的代码中还有一行
pd.DataFrame(CV_data.take(5), columns=CV_data.columns)
。
This line creates a dataframe and then discards it.此行创建一个数据帧,然后将其丢弃。 Even if you were successfully calling
to_csv
, none of your changes to CV_data
would have been reflected in that dataframe (and therefore in the outputed csv file).即使您成功调用
to_csv
,您对CV_data
任何更改CV_data
不会反映在该数据CV_data
(因此也不会反映在输出的 csv 文件中)。
This will do the job!这将完成工作!
#Create a DataFrame:
new_df = pd.DataFrame({'id': [1,2,3,4,5], 'LETTERS': ['A','B','C','D','E'], 'letters': ['a','b','c','d','e']})
#Save it as csv in your folder:
new_df.to_csv('C:\\Users\\You\\Desktop\\new_df.csv')
Solution- You should write df.to_csv instead of pd.to_csv解决方案-您应该编写 df.to_csv 而不是 pd.to_csv
Justification- to_csv is a method to an object which is a df (DataFrame); Justification- to_csv 是一个对象的方法,它是一个 df (DataFrame); while pd is Panda module.
而 pd 是 Panda 模块。
Hence, your code was not working and throwing this Error " AttributeError: module 'pandas' has no attribute 'to_csv'"因此,您的代码无法正常工作并抛出此错误“AttributeError: module 'pandas' has no attribute 'to_csv'”
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