def testappendDFReturnsEmptyDataFame(self):
stocksData = {
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.appendDF(dataframe=df,appendDataFrame=df)
expected = pd.DataFrame(df).append(df)
def testdropDuplicateDFReturnsEmptyDataFame(self):
stocksData = {
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
df = npsy.appendDF(dataframe=df,appendDataFrame=df)
actual = npsy.dropDuplicatesDF(df)
expected = pd.DataFrame(stocksData).append(df).drop_duplicates()
def testdropDuplicateReturnsDataFame(self):
stockData = {
"Blackstone": [49.56, 50.70, 51.18, 52.80, 52.87, 24.24, 24.60, 49.56, 26.90, 26.66]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
df = npsy.appendDF(dataframe=df,appendDataFrame=df)
actual = npsy.dropDuplicatesDF(df)
expected = pd.DataFrame(stockData).append(df).drop_duplicates()
def testappendDFReturnsDataFame(self):
stocksData = {
"Blackstone": [49.56, 50.70, 51.18, 52.80, 52.87, 24.24, 24.60, 26.04, 26.90, 26.66],
"KKR": [24.24, 24.60, 26.04, 26.90, 26.66, 24.24, 24.60, 26.04, 26.90, 26.66]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.appendDF(dataframe=df,appendDataFrame=df)
expected = pd.DataFrame(stocksData).append(df)