Pandas

Question 1

Creating a Series

Answer 1

pd.series(list)

pd.Series(data=my_list,index=labels)

Question 2

Creating series along with the index

Answer 2

ser1 = pd.Series([1,2,3,4],index = [‘USA’, ‘Germany’,’USSR’, ‘Japan’])

ser1

USA 1
Germany 2
USSR 3
Japan 4

dtype: int64

Question 3

fetch elements in series

Answer 3

varname[index]

ser1[‘USA’]

Question 4

Create a DataFrame

Answer 4

pd.DataFrame(data, index= , columns = )

pd.DataFrame(randn(5,4),index=’A B C D E’.split(),columns=’W X Y Z’.split())

          W                X                     Y                    Z A     2.706850     0.628133     0.907969     0.503826 B     0.651118     -0.319318    -0.848077    0.605965 C     -2.018168     0.740122     0.528813    -0.589001 D     0.188695     -0.758872     -0.933237   0.955057 E     0.190794     1.978757       2.605967    0.683509

Question 5

fetching column from a dataframe

Answer 5

dataframe[col_name] –> df[‘W’]

dataframe[[col1,col2 ]] –> df[[‘W’,’Z’]]

Question 6

Creating a new column in a dataframe

Answer 6

df[‘new’] = df[‘W’] + df[‘Y’]

Question 7

how to remove a column

Answer 7

df.drop(‘col_name’,axis=1,inplace=True)

Question 8

how to remove a rows

Answer 8

df.drop(‘index’, axis=0)

Question 9

Selecting Rows from a DataFrame

Answer 9

df.loc[“index”]

df.loc[“label”]

Question 10

Selecting multiple Rows and column in a DataFrame

Answer 10

df.loc[row, col]

df.loc[ [row1,row2,… ] , [col1,col2,… ] ]

df.loc[[‘A’,’B’],[‘W’,’Y’]]

Question 11

Conditional Selection

Answer 11

An important feature of pandas is conditional selection using bracket notation, very similar
to numpy:

dataFrame[condition]

df[df>0]

df[df[‘col’] >0]

Question 12

Selecting single and multiple columns

Answer 12

dataframe[ dataframe [“col_name”] < 5] [ ‘colname’]

df[df[‘W’]>0][‘Y’]

dataframe[ dataframe [“col_name”] < 5] [ [‘col1’,col2’] ]

df[df[‘W’]>0][[‘Y’,’X’]]

Question 13

Selecting single and multiple columns with multiple conditions

Answer 13

For two conditions you can use | and & with parenthesis:

dataframe[ (condition1) & (condition2)]

df[(df[‘W’]>0) & (df[‘Y’] > 1)]

Question 14

how to reset index

Answer 14

Reset to default 0,1…n index

dataframe.reset_index()

df.reset_index()

Question 15

how to set specific column as index in a dataframe

Answer 15

Here we can give column name or we can provide pandas series
Dataframe.set_index(“col_name”)

df.set_index(‘States’)

df.set_index(‘States’,inplace=True) to change in permanently

Question 16

how to create Multi-Index in a dataframe

Answer 16

zip the 2 list convert to list of tuples

first create 2 list outside and inside index
outside = [‘G1’,’G1’,’G1’,’G2’,’G2’,’G2’]
inside = [1,2,3,1,2,3]
hier_index = list(zip(outside,inside))
#set multiindex from tuple
hier_index = pd.MultiIndex.from_tuples(hier_index)

print(pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=[‘A’,’B’]))

A B
G1 1 -0.419202 -0.516732
2 0.036076 -0.205218
3 0.148782 0.942314
G2 1 0.001280 -1.956888
2 1.069982 -2.220442
3 1.180214 0.924161

Question 17

How to fetch elements from the Multi-Index dataframe

Answer 17

For index hierarchy we use df.loc[], if this was on the
columns axis, you would just use normal bracket notation df[]. Calling one level of the index
returns the sub-dataframe:

df.loc[“G1”]

df.loc[‘G1’].loc[1]

Question 18

how to drop missing values

Answer 18

df.dropna() This deletes from row wise

df.dropna(axis=1) this deletes from column wise

df.dropna(thresh=2) removes rows having 2 missing values

Question 19

how to replace null values

Answer 19

df.fillna(value=’FILL VALUE’)

df[‘A’].fillna(value=df[‘A’].mean())

Question 20

hello

Groupby

ok

this

Answer 20

data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
'Sales':[200,120,340,124,243,350]}

df.groupby('Company')

Now you can use the .groupby() method to group rows together based off of a column
name. For instance let’s group based off of Company. This will create a DataFrameGroupBy
object

Company Person Sales
GOOG   Sam 200 
GOOG   Charlie 120
MSFT   Amy 340
MSFT   Vanessa 124
FB   Carl 243
FB   Sarah 350

df.groupby("Company").mean()

And then call aggregate methods off the object. we have to use any one aggregate methods

Question 21

what are the aggregate methods used in group by

Answer 21

std(), min(), max(), count(), describe()

Question 22

how to you transpose the dataframe

Answer 22

dataFrame.transpose()

Question 23

what are the ways to combine dataframes together

Answer 23

Merging, Joining, and Concatenating

Question 24

how to concatenate dataframes

Answer 24

Concatenation basically glues together DataFrames. Keep in mind that dimensions should
match along the axis you are concatenating on. You can use pd.concat and pass in a list of
DataFrames to concatenate together

pd.concat([dataframe1], [dataframe2], [dataframe3], ..)

pd.concat([dataframe1], [dataframe2], [dataframe3], .., axis=1)

Question 25

Merging multiple dataframes

Answer 25

The merge function allows you to merge DataFrames together using a similar logic as merging SQL Tables together. For example:

pd.merge(left,right, how = “inner”, on = “col_name”)

Answer 26

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
'key2': ['K0', 'K0', 'K0', 'K0'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})

pd.merge(left, right, on=['key1', 'key2'])

Here above it merges side by side

Question 27

how to fetch the unique values and its count

Answer 27

dataframe[“col_name”].unique() –> fetching unique values

dataframe[“col_name”].nunique() –> fetching count of unique values

df[‘col2’].value_counts() –> fetching values and count

Question 28

how to Select from DataFrame using criteria from multiple columns

Answer 28

dataframe[(dataframe[‘col1’]>2) & (dataframe[‘col2’]==444)]

df[(df[‘col1’]>2) & (df[‘col2’]==444)]

Question 29

how to use apply functions in pandas

Answer 29

dataframe[“col_name”].apply(function_name)

every data in the column will apply this function

df[‘col3’].apply(len)

df[‘col3’].apply(lambda x: len(x))

Question 30

how to Permanently Remove` a Column

Answer 30

del df[‘col_name’]

Question 31

how to Get column and index names

Answer 31

dataFrame.columns
RangeIndex(start=0, stop=2, step=1)

dataframe.index
Index([‘col2’, ‘col3’], dtype=’object’)

Question 32

how to Sorting and Ordering a DataFrame

Answer 32

dataframe.sort_values(by = “colname”)
#inplace=False by default

Question 33

how to Find Null Values or Check for Null Values

Answer 33

dataframe.isnull()

Question 34

how to drop null rows

Answer 34

df.dropna()

Question 35

how to Fill in Null values with something else

Answer 35

df.fillna(“value”)

Question 36

how to create pivot table

Answer 36

data = {‘A’:[‘foo’,’foo’,’foo’,’bar’,’bar’,’bar’],
‘B’:[‘one’,’one’,’two’,’two’,’one’,’one’],
‘C’:[‘x’,’y’,’x’,’y’,’x’,’y’],
‘D’:[1,3,2,5,4,1]}
df = pd.DataFrame(data)

A   B    C  D 0 foo one x 1 1 foo one y 3 2 foo two x 2 3 bar two y 5 4 bar one x 4 5 bar one y 1

df.pivot_table(values=’D’,index=[‘A’, ‘B’],columns=[‘C’])

	 C     x     y A      B bar  one 4.0 1.0
two NaN 5.0 foo one 1.0 3.0
two 2.0 NaN

Question 37

importing csv_file and exporting csv file in pandas

Answer 37

pd.read_csv(file_name)

dataframe.to_csv(file_name, index=False)

Question 38

importing excel_file and exporting excel file in pandas

Answer 38

Pandas can read and write excel files, keep in mind, this only imports data. Not formulas or
images, having images or macros may cause this read_excel method to crash.

pd.read_excel(‘Excel_Sample.xlsx’,sheetname=’Sheet1’)

dataframe.to_excel(‘Excel_Sample.xlsx’,sheet_name=’Sheet1’)

Question 39

how to read html tables from the website

Answer 39

Pandas read_html function will read tables off of a webpage and return a list of DataFrame
objects:
df = pd.read_html(‘website link’)