>>>?x?=?np.array([12,?10,?12,?0,?6,?8,?9,?1,?16,?4,?6,?0])
>>>?index_val?=?np.argpartition(x,?-4)[-4:]
>>>?index_val
array([1,?8,?2,?0],?dtype=int64)
>>>?np.sort(x[index_val])
array([10,?12,?12,?16])

>>>?array1?=?np.array([0.12,0.17,0.24,0.29])
>>>?array2?=?np.array([0.13,0.19,0.26,0.31])#?with?a?tolerance?of?0.1,?it?should?return?False:
>>>?np.allclose(array1,array2,0.1)
False#?with?a?tolerance?of?0.2,?it?should?return?True:
>>>?np.allclose(array1,array2,0.2)
True

>>>?x?=?np.array([3,?17,?14,?23,?2,?2,?6,?8,?1,?2,?16,?0])
>>>?np.clip(x,2,5)
array([3,?5,?5,?5,?2,?2,?5,?5,?2,?2,?5,?2])

#?Random?integers
>>>?array?=?np.random.randint(20,?size=12)
>>>?array
array([?0,??1,??8,?19,?16,?18,?10,?11,??2,?13,?14,??3])#??Divide?by?2?and?check?if?remainder?is?1
>>>?cond?=?np.mod(array,?2)==1
>>>?cond
array([False,??True,?False,??True,?False,?False,
???????False,??True,?False,?True,?False,??True])#?Use?extract?to?get?the?values
>>>?np.extract(cond,?array)
array([?1,?19,?11,?13,??3])#?Apply?condition?on?extract?directly
>>>?np.extract(((array?<?3)?|?(array?>?15)),?array)
array([?0,??1,?19,?16,?18,??2])

>>>?y?=?np.array([1,5,6,8,1,7,3,6,9])?#?Where?y?is?greater?than?5,?returns?index?position
>>>?np.where(y>5)
array([2,?3,?5,?7,?8],?dtype=int64)?#?First?will?replace?the?values?that?match?the?condition,?
#?second?will?replace?the?values?that?does?not
>>>?np.where(y>5,?"Hit",?"Miss")
array(['Miss',?'Miss',?'Hit',?'Hit',?'Miss',
???????'Hit',?'Miss',?'Hit',?'Hit'],
???????dtype='<U4')

>>>?a?=?np.array([1,5,6,8,1,7,3,6,9])
>>>?print("50th?Percentile?of?a,?axis?=?0?:?",??
...???????np.percentile(a,?50,?axis?=0))
50th?Percentile?of?a,?axis?=?0?:??6.0
>>>?b?=?np.array([[10,?7,?4],?[3,?2,?1]])
>>>?print("30th?Percentile?of?b,?axis?=?0?:?",??
...???????np.percentile(b,?30,?axis?=0))
30th?Percentile?of?b,?axis?=?0?:??[5.1?3.5?1.9]

import?io
import?requests
#?I?am?using?this?online?data?set?just?to?make?things?easier?for?you?guys
url?=?"https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/datasets/AirPassengers.csv"
s?=?requests.get(url).content#?read?only?first?10?rows
df?=?pd.read_csv(io.StringIO(s.decode('utf-8')),nrows=10?,?index_col=0)

#?create?a?dataframe
dframe?=?pd.DataFrame(np.random.randn(4,?3),?
??????????????????????columns=list('bde'),?
??????????????????????index=['India',?'USA',?'China',?'Russia'])
#compute?a?formatted?string?from?each?floating?point?value?in?frame
changefn?=?lambda?x:?'%.2f'?%?x
#?Make?changes?element-wise
dframe['d'].map(changefn)

#?max?minus?mix?lambda?fn
fn?=?lambda?x:?x.max()?-?x.min()
#?Apply?this?on?dframe?that?we've?just?created?above
dframe.apply(fn)

#?Using?the?dataframe?we?created?for?read_csv
filter1?=?df["value"].isin([112])?
filter2?=?df["time"].isin([1949.000000])df?[filter1?&?filter2]

#?creating?sample?series?
data?=?pd.Series(['India',?'Pakistan',?'China',?'Mongolia'])
#?Assigning?issue?that?we?face
data1=?data
#?Change?a?value
data1[0]='USA'
#?Also?changes?value?in?old?data
framedata
#?To?prevent?that,?we?use
#?creating?copy?of?series?
new?=?data.copy()
#?assigning?new?values?
new[1]='Changed?value'
#printing?data?
print(new)?
print(data)

#?We'll?use?the?same?dataframe?that?we?used?for?read_csv
framex?=??df.select_dtypes(include="float64")
#?Returns?only?time?column

#?Create?a?sample?dataframe
school?=?pd.DataFrame({'A':?['Jay',?'Usher',?'Nicky',?'Romero',?'Will'],?
??????'B':?['Masters',?'Graduate',?'Graduate',?'Masters',?'Graduate'],?
??????'C':?[26,?22,?20,?23,?24]})#?Lets?create?a?pivot?table?to?segregate?students?based?on?age?and?course
table?=?pd.pivot_table(school,?values?='A',?index?=['B',?'C'],?
?????????????????????????columns?=['B'],?aggfunc?=?np.sum,?fill_value="Not?Available")
table