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| Code Block |
|---|
import pandas as pd
import numpy as np
# Create a DataFrame
d = {
'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
'Score':[85,63,55,74,31,77,85,63,42,62,89,77]
}
df = pd.DataFrame(d,columns=['Name','Age','Score'])
# get the maximum values of all the column in dataframe - it will be raghu, 26, 89, object
df.max()
# get the maximum value of the column 'Age' - it will be 26
df['Age'].max()
# get the maximum value of the column 'Name' - it will be raghu
df['Name'].max() |
Get the minimum value of column in Pandas DataFrame
| Code Block |
|---|
import pandas as pd
import numpy as np
# Create a DataFrame
d = {
'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
'Score':[85,63,55,74,31,77,85,63,42,62,89,77]
}
df = pd.DataFrame(d,columns=['Name','Age','Score'])
# get the minimum values of all the column in dataframe - it will display Alex, 22, 31, object
df.min()
# get the minimum value of the column 'Age' - it will be 22
df['Age'].min()
# get the minimum value of the column 'Name' - it will be Alex
df['Name'].min() |
Select row with maximum and minimum value in Pandas DataFrame
| Code Block |
|---|
import pandas as pd
import numpy as np
#Create a DataFrame
d = {
'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
df = pd.DataFrame(d,columns=['Name','Age','Score'])
# get the row of max value
df.loc[df['Score'].idxmax()]
# get the row of minimum value
df.loc[df['Score'].idxmin()] |
Get the unique values (rows) of a Pandas Dataframe
| Code Block |
|---|
Create Dataframe:
import pandas as pd
import numpy as np
#Create a DataFrame
d = {
'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
'Age':[26,24,23,22,23,24,26,24,22,23,24,24]
}
df = pd.DataFrame(d,columns=['Name','Age'])
# get the unique values (rows)
print df.drop_duplicates()
# get the unique values (rows) by retaining last row
print df.drop_duplicates(keep='last') |
Get the list of column headers or column name in a Pandas DataFrame
| Code Block |
|---|
import pandas as pd
import numpy as np
#Create a DataFrame
d = {
'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
df = pd.DataFrame(d,columns=['Name','Age','Score'])
# method 1: get list of column name
list(df.columns.values)
# method 2: get list of column name
list(df) |
Delete or Drop the duplicate row of a Pandas DataFrame
| Code Block |
|---|
import pandas as pd
import numpy as np
#Create a DataFrame
d = {
'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
df = pd.DataFrame(d,columns=['Name','Age','Score'])
# drop duplicate rows
df.drop_duplicates()
# drop duplicate rows by retaining last occurrence
df.drop_duplicates(keep='last')
# drop duplicate by a column name
df.drop_duplicates(['Name'], keep='last') |
Drop or delete the row in Pandas DataFrame with conditions
| Code Block |
|---|
import numpy as np
#Create a DataFrame
d = {
'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine',
'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'],
'Age':[26,24,23,22,23,24,26,24,22,23,24,24],
'Score':[85,63,55,74,31,77,85,63,42,62,89,77]}
df = pd.DataFrame(d,columns=['Name','Age','Score'])
# Drop an observation or row
df.drop([1,2])
# Drop a row by condition
df[df.Name != 'Alisa']
# Drop a row by index
df.drop(df.index[2])
# Drop bottom 3 rows
df[:-3] |
Generator
Random number generation
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