def testSingleValDictDataFrameCorrReturnSerie(self):
stockData = {"Blackstone": [49.56]
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.countDF(df)
expected = pd.DataFrame(stockData).corr()
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 testEmptyTailDFReturnsTailDataFame(self):
stocksData ={
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.tailDF(df)
expected = pd.DataFrame(stocksData).tail()
def testEmptyDataFrameCorrReturnEmptySeries(self):
stockData = {
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.countDF(df)
expected = pd.DataFrame(stockData).corr()
def testappendDFReturnsEmptyDataFame(self):
stocksData = {
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.appendDF(dataframe=df,appendDataFrame=df)
expected = pd.DataFrame(df).append(df)
def testEmptyHeadDFReturnsHeadDataFame(self):
stocksData ={
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stocksData)
actual = npsy.headDF(df)
expected = pd.DataFrame(stocksData).head()
def testaverageDFReturnsEmptySeriesMean(self):
stockData ={
}
npsy = NumpySciPy()
df = npsy.initDataFrame(stockData)
actual = npsy.averageDF(df)
expected = pd.DataFrame(stockData).mean()
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 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 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 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 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 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 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 testEmptyDataInitDataFrameReturnsDataFame(self):
stocksData ={
}
npsy = NumpySciPy()
actual = npsy.initDataFrame(stocksData)
expected = pd.DataFrame(stocksData)
