def testDictDataFrameCorrReturnSerie(self):
stockData = {"Blackstone": [49.56, 50.70, 51.18, 52.80, 52.87]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.countDF(df)
expected = pd.DataFrame(stockData).corr()
def testSingleValDictDataFrameCorrReturnSerie(self):
stockData = {"Blackstone": [49.56]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.countDF(df)
expected = pd.DataFrame(stockData).corr()
def testaverageDFReturnsEmptySeriesMean(self):
stockData ={
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.averageDF(df)
expected = pd.DataFrame(stockData).mean()
def testappendDFReturnsEmptyDataFame(self):
stocksData = {
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.appendDF(dataframe=df,appendDataFrame=df)
expected = pd.DataFrame(df).append(df)
def testEmptyTailDFReturnsTailDataFame(self):
stocksData ={
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.tailDF(df)
expected = pd.DataFrame(stocksData).tail()
def testEmptyHeadDFReturnsHeadDataFame(self):
stocksData ={
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.headDF(df)
expected = pd.DataFrame(stocksData).head()
def testEmptyDataFrameCorrReturnEmptySeries(self):
stockData = {
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.countDF(df)
expected = pd.DataFrame(stockData).corr()
def testDualArrayDictDataFrameCorrReturnSeries(self):
stockData = {"Blackstone": [49.56, 50.70, 51.18, 52.80, 52.87],
"KKR": [24.24, 24.60, 26.04, 26.90, 26.66]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.countDF(df)
expected = pd.DataFrame(stockData).corr()
def testaverageDFReturnsSeriesMean(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)
actual = npsy.averageDF(df)
expected = pd.DataFrame(stockData).mean()
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 testNotFilterColumnDFForCompanyBlackstone(self):
stockData = {
'Company': ["BlackStone","KKR","Chase","Bank Of America","Wells Fargo","Morgan Stanley"],
"Closing Price":[56.26,21.60,100.21,26.75,84.61,246.25],
"LocationHQ": ["New York City,NY","New York City,NY","New York City,NY","Charlotte, NC", "San Francisco, CA","New York City,NY"]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.notFilterColumnDF(df,"Company","BlackStone")
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)
def testInitDataFrameReturnsDataFameWithDates(self):
stocksData ={
"Blackstone":[49.56,50.70,51.18,52.80,52.87],
"KKR": [24.24,24.60,26.04,26.90,26.66]
}
dates = ['5/4/20','5/5/20','5/6/20','5/8/20','5/9/20']
npsy = NumpySciPy()
actual = npsy.initDataFrame(stocksData,rows=dates)
expected = pd.DataFrame(stocksData,index=dates)
def testDefaul10TailDFReturnsTailDataFame(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]
}
dates = ['5/4/20', '5/5/20', '5/6/20', '5/8/20', '5/9/20','5/4/20', '5/5/20', '5/6/20', '5/8/20', '5/9/20']
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData, rows=dates)
actual = npsy.tailDF(df,)
expected = pd.DataFrame(stocksData, index=dates).tail(10)
def testInitDataFrameReturnsDataFame(self):
stocksData ={
"Blackstone":[49.56,50.70,51.18,52.80,52.87],
"KKR": [24.24,24.60,26.04,26.90,26.66]
}
npsy = NumpySciPy()
actual = npsy.initDataFrame(stocksData)
print(actual)
expected = pd.DataFrame(stocksData)
print(expected)
def testFilterColumnDFAdvForCompanyEmptyDF(self):
stockData = {
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
try:
actual = npsy.filterColumnDFAdv(df, "Company", "BlackStone")
print(actual)
except(KeyError):
print("Key Doesn't Exist")
def testFilterColumnDFAdvForNotEqualsClosingPrice(self):
stockData = {
'Company': ["BlackStone", "KKR", "Chase", "Bank Of America", "Wells Fargo", "Morgan Stanley"],
"Closing Price": [56.26, 21.60, 100.21, 26.75, 84.61, 246.25],
"LocationHQ": ["New York City,NY", "New York City,NY", "New York City,NY", "Charlotte, NC",
"San Francisco, CA", "New York City,NY"]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.filterColumnDFAdv(df, "Closing Price", 100.21,filterType="NOT_EQUALS")
print(actual)
def test_emptyArrayNumpyArray_returns_array(self):
npsp = NumpySciPy()
arrays = []
actual = npsp.numpyArray(arrays)
expected = np.array(arrays)
boolArray= actual == expected
isTrue =None
if False in boolArray:
isTrue = False
else:
isTrue = True
self.assertTrue(isTrue)
def testNotFilterColumnDFForOpenPricePrice1226(self):
stockData = {
'Company': ["BlackStone", "KKR", "Chase", "Bank Of America", "Wells Fargo", "Morgan Stanley"],
"Closing Price": [56.26, 21.60, 100.21, 56.26, 84.61, 246.25],
"LocationHQ": ["New York City,NY", "New York City,NY", "New York City,NY", "Charlotte, NC",
"San Francisco, CA", "New York City,NY"]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
try:
actual = npsy.notFilterColumnDF(df, "Opening Price", 12.26)
except(KeyError):
print("Key Doesn't Exist")
def testNumpyAverageMixTypesValueArray(self):
npsy = NumpySciPy()
multiArray = [[0.01,1],[0.01,1]]
actual = npsy.numpyAverage(multiArray)
expected = 0.505
self.assertEqual(expected,actual)
def testNumpyAverageOneValueArray(self):
npsy = NumpySciPy()
multiArray = [[1,2],[1,2]]
actual = npsy.numpyAverage(multiArray)
expected = 1.5
self.assertEqual(expected,actual)
def testNumpyAveragetwoValueArray(self):
npsy = NumpySciPy()
emptyArray = [[1,2]]
actual = npsy.numpyAverage(emptyArray)
expected = 1.5
self.assertEqual(expected,actual)
def testNumpyAverageEmptyArray(self):
npsy = NumpySciPy()
emptyArray = []
isNaN = np.isnan(npsy.numpyAverage(emptyArray))
self.assertTrue(isNaN)
def testNumpySizeArray3_returns_size1(self):
npsy = NumpySciPy()
emptyArray = [[1],[2],[3]]
actual = npsy.numpySize(emptyArray)
expected = 3
self.assertEqual(expected, actual)
def testNumpySizeEmptyArray_returns_size0(self):
npsy = NumpySciPy()
emptyArray = []
actual = npsy.numpySize(emptyArray)
expected = 0
self.assertEqual(expected,actual)
def testArrayInitSeriesreturnSeries(self):
priceArray = [49.56, 50.70, 51.18, 52.80, 52.87]
npsy = NumpySciPy()
actual = npsy.initSeries(priceArray)
expected = pd.Index(priceArray)
def test2DArrayInitSeriesreturnObjectSeries(self):
priceArray = [[49.56, 50.70, 51.18, 52.80, 52.87],[24.24, 24.60, 26.04, 26.90, 26.66]]
npsy = NumpySciPy()
actual = npsy.initSeries(priceArray)
expected = pd.Index(priceArray)
def testSingleValueArrayValueCountSeriesreturnSerie(self):
priceArray = [52.80]
npsy = NumpySciPy()
series = npsy.initSeries(priceArray)
actual = npsy.valueCountSeries(series)
expected = pd.Index(priceArray).value_counts()
def testArrayvalueCountSeriesreturnEmptySeries(self):
priceArray = [52.80, 52.80, 51.18, 52.80,51.18,52.87]
npsy = NumpySciPy()
series = npsy.initSeries(priceArray)
actual = npsy.valueCountSeries(series)
expected = pd.Index(priceArray).value_counts()
