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How to find maximum value of a column in python dataframe
(4 answers)
Closed 1 year ago.
I was given a task where I'm supposed to find largest value in 'Salary' column and print the value.
Here's the code:
import numpy as np
import pandas as pd
# TODO: Find the largest number in 'Salary' column
data_one = pd.read_excel("C:\\Users\\HP\\Documents\\DataScience task\\Employee.xlsx")
dataframe_data = pd.DataFrame(data_one)
find_largest_salary = dataframe_data['Salary']
Output:
First Name Last Name Gender Age Experience (Years) Salary
0 Arnold Carter Male 21 10 8344
1 Arthur Farrell Male 20 7 6437
2 Richard Perry Male 28 3 8338
3 Ellia Thomas Female 26 4 8870
4 Jacob Kelly Male 21 4 548
... ... ... ... ... ... ...
95 Leonardo Barnes Male 23 6 7120
96 Kristian Mason Male 21 7 7018
97 Paul Perkins Male 21 6 2929
98 Justin Moore Male 25 0 3141
99 Naomi Ryan Female 22 10 7486
The output I wanted (example):
99999
the way you have described the question, the answer is simply this:
find_largest_salary = dataframe_data['Salary'].max()
Related
Two separate similar DataFrames with different lengths
df2=
pd.DataFrame([('James',25,'Male',155),
('John',27, 'Male',175),
('Patricia',23,'Female',135),
('Mary',22,'Female',125),
('Martin',30,'Male',185),
('Margaret',29,'Female'141),
('Kevin',22,'Male',198)], columns =(['First Name','Age','Gender','Weight']))
Index
First Name
Age
Gender
Weight
0
James
25
Male
155
1
John
27
Male
175
2
Patricia
23
Female
135
3
Mary
22
Female
125
4
Martin
30
Male
185
5
Margaret
29
Female
141
6
Kevin
22
Male
198
df1=
pd.DataFrame([('James',25,'Male',165,"5'10"),
('John',27, 'Male',175,"5'9"),
('Matthew',29,'Male',183,"6'0"),
('Patricia',23,'Female',135,"5'3"),
('Mary',22,'Female',125,"5'4"),
('Rachel',29,'Female',123,"5'3"),
('Jose',20,'Male',175,"5'11"),
('Kevin',22,'Male',192,"6'2")], columns =(['First Name','Age','Gender','Weight','Height']))
Index
First Name
Age
Gender
Weight
Height
0
James
25
Male
165
5'10
1
John
27
Male
175
5'9
2
Matthew
29
Male
183
6'0
3
Patricia
23
Female
135
5'3
4
Mary
22
Female
125
5'4
5
Rachel
29
Female
123
5'3
6
Jose
20
Male
175
5'11
7
Kevin
22
Male
192
6'2
df2 has some rows which are not in df1 and df1 has some values which are not in df2.
I need to calculate the modified values, if the First Name is same, I need to check for modified values; for example in df1, the weight of James is 155 however in df2 the weight is 165, so I need to store the modified weight of James(165) and index(0) in a new dataframe without iteration; the iteration takes a long time because this is a sample of a big dataframe with a lot more rows and columns.
Desired output:
df2=
Index
First Name
Age
Gender
Weight
Height
0
James
25
Male
155
5'10
1
John
27
Male
175
5'9
2
Patricia
23
Female
135
5'3
3
Mary
22
Female
125
5'4
4
Martin
30
Male
185
5
Margaret
29
Female
141
6
Kevin
22
Male
198
6'2
Martin's and Margaret's heights are not there in df1, so their heights are not updated in df2
Desired Output
modval=
Index
First Name
Age
Gender
Weight
Height
0
James
165
7
Kevin
192
I have a data set which has values for different columns as different entries with first name to identify the respective columns.
For instance James's gender is in first row and James's age is in 5th row.
DataFrame
df1=
Index
First Name
Age
Gender
Weight in lb
Height in cm
0
James
Male
1
John
175
2
Patricia
23
5
James
22
4
James
185
5
John
29
6
John
176
I am trying to make it combined into one DataFrame as below
df1=
Index
First Name
Age
Gender
Weight
Height
0
James
22
Male
185
1
John
29
175
176
2
Patricia
23
I tried to do groupby but it is not working.
Assuming NaN in the empty cells, you can use groupby.first:
df.groupby('First Name', as_index=False).first()
output:
First Name Age Gender Weight in lb Height in cm
0 James 22.0 Male 185.0 NaN
1 John 29.0 None 175.0 176.0
2 Patricia 23.0 None NaN NaN
Two seperate similar DataFrames with different lengths
df2=
Index
First Name
Age
Gender
Weight
0
James
25
Male
155
1
John
27
Male
175
2
Patricia
23
Female
135
3
Mary
22
Female
125
4
Martin
30
Male
185
5
Margaret
29
Female
141
6
Kevin
22
Male
198
df1=
Index
First Name
Age
Gender
Weight
Height
0
James
25
Male
165
5'10
1
John
27
Male
175
5'9
2
Matthew
29
Male
183
6'0
3
Patricia
23
Female
135
5'3
4
Mary
22
Female
125
5'4
5
Rachel
29
Female
123
5'3
6
Jose
20
Male
175
5'11
7
Kevin
22
Male
192
6'2
df2 has some rows which are not in df1 and df1 has some values which are not in df2.
I am comparing df1 against df2. I have calculated the newentries with the following code
newentries = df2.loc[~df2['First Name'].isin(df1['First Name'])]
deletedentries = df1.loc[~df1['First Name'].isin(df2['First Name'])]
where newentries denote the rows/entries that are there in df2 but not in df1; deletedentries denote the rows/entries that are there in df1 but not in df2. The above code works perfectly fine.
I need to copy the height from df1 to df2 when the first names are equal.
df2.loc[df2['First Name'].isin(df1['First Name']),"Height"] = df1.loc[df1['First Name'].isin(df2['First Name']),"Height"]
The above code copies the values however indexing is causing an issue and the values are not copied to the corresponding rows, I tried to promote First Name as the Index but that doesn't solve the issue. Please help me with a solution
Also, I need to calculate the modified values, if the First Name is same, I need to check for modified values; for example in df1, the weight of James is 155 however in df2 the weight is 165, so I need to store the modified weight of James(165) and index(0) in a new dataframe without iteration; the iteration takes a long time because this is a sample of a big dataframe with a lot more rows and columns.
Desired output:
df2=
Index
First Name
Age
Gender
Weight
Height
0
James
25
Male
155
5'10
1
John
27
Male
175
5'9
2
Patricia
23
Female
135
5'3
3
Mary
22
Female
125
5'4
4
Martin
30
Male
185
5
Margaret
29
Female
141
6
Kevin
22
Male
198
6'2
Martin's and Margaret's heights are not there in df1, so their heights are not updated in df2
newentries=
Index
First Name
Age
Gender
Weight
Height
4
Martin
30
Male
185
5
Margaret
29
Female
141
deletedentries=
Index
First Name
Age
Gender
Weight
Height
2
Matthew
29
Male
183
6'0
5
Rachel
29
Male
123
5'3
6
Jose
20
Male
175
5'11
modval=
Index
First Name
Age
Gender
Weight
Height
0
James
165
7
Kevin
192
Building off of Rabinzel's answer:
output = df2.merge(df1, how='left', on='First Name', suffixes=[None, '_old'])
df3 = output[['First Name', 'Age', 'Gender', 'Weight', 'Height']]
cols = df1.columns[1:-1]
modval = pd.DataFrame()
for col in cols:
modval = pd.concat([modval, output[['First Name', col + '_old']][output[col] != output[col + '_old']].dropna()])
modval.rename(columns={col +'_old':col}, inplace=True)
newentries = df2[~df2['First Name'].isin(df1['First Name'])]
deletedentries = df1[~df1['First Name'].isin(df2['First Name'])]
print(df3, newentries, deletedentries, modval, sep='\n\n')
Output:
First Name Age Gender Weight Height
0 James 25 Male 155 5'10
1 John 27 Male 175 5'9
2 Patricia 23 Female 135 5'3
3 Mary 22 Female 125 5'4
4 Martin 30 Male 185 NaN
5 Margaret 29 Female 141 NaN
6 Kevin 22 Male 198 6'2
First Name Age Gender Weight
4 Martin 30 Male 185
5 Margaret 29 Female 141
First Name Age Gender Weight Height
2 Matthew 29 Male 183 6'0
5 Rachel 29 Male 123 5'3
6 Jose 20 Male 175 5'11
First Name Age Gender Weight
0 James NaN NaN 165.0
6 Kevin NaN NaN 192.0
for your desired output for df2 you can try this:
desired_df2 = df2.merge(df1[['First Name','Height']], on='First Name', how='left')
#if you want to change the "NaN" values just add ".fillna(fill_value=0)" for e.g 0 after the merge
print(desired_df2)
First Name Age Gender Weight Height
0 James 25 Male 155 5'10
1 John 27 Male 175 5'9
2 Patricia 23 Female 135 5'3
3 Mary 22 Female 125 5'4
4 Martin 30 Male 185 NaN
5 Margaret 29 Female 141 NaN
6 Kevin 22 Male 198 6'2
new and deleted entries is already right. for the moment I'm a bit stuck how to get the modval dataframe. I'll update my answer if I get a solution.
I have a dataframe which looks like this :
Name Age Job
0 Alex 20 Student
1 Sara 21 Doctor
2 john 23 NaN
3 kevin 22 Teacher
4 Rosa 20 senior manager
5 johanes 25 Dentist
6 lina 23 Student
7 yaser 25 Pilot
8 jason 20 Manager
9 Ali 23 NaN
10 Ahmad 21 Professor
11 Joe 24 NaN
12 Donald 29 Waiter
.
.
.
.
I want to select the rows before and after the row that has NaN values in column Job with the row itself. For that I have the following code :
Rows = df[df. Shift(1, fill_value="dummy").Job. isna() | df.Job. isna()| df. Shift(-1, fill_value="dummy"). df. isna()]
print(Rows)
the result is this:
1 Sara 21 Doctor
2 john 23 NaN
3 kevin 22 Teacher
8 jason 20 Manager
9 Ali 23 NaN
10 Ahmad 21 Professor
11 Joe 24 NaN
12 Donald 29 Waiter
The only problem here is the row number 10, it should be double in the result because this row is one time the row after NaN which is number 9 and at the same time the row before NaN value which is row number 11( the row is between two rows with NaN value). So at the end I want to have this :
1 Sara 21 Doctor
2 john 23 NaN
3 kevin 22 Teacher
8 jason 20 Manager
9 Ali 23 NaN
10 Ahmad 21 Professor
10 Ahmad 21 Professor
11 Joe 24 NaN
12 Donald 29 Waiter
So every row which is between two rows with NaN values should be also two times in the result (or should be dupplicate). Is there any way to do this? Any help will be appreciated.
Use concat with rows before, after and match by condition:
m = df.Job.isna()
df = pd.concat([df[m.shift(fill_value=False)],
df[m.shift(-1, fill_value=False)],
df[m]]).sort_index()
print (df)
Name Age Job
1 Sara 21 Doctor
2 john 23 NaN
3 kevin 22 Teacher
8 jason 20 Manager
9 Ali 23 NaN
10 Ahmad 21 Professor
10 Ahmad 21 Professor
11 Joe 24 NaN
12 Donald 29 Waiter
I have a dictionary of DataFrame objects:
dictDF={0:df0,1:df1,2:df2}
Each DataFrame df0,df1,df2 represents a table in a specific date of time, where the first column identifies (like social security number) a person and the other columns are characteristics of this person such as
DataFrame df0
id Name Age Gender Job Income
10 Daniel 40 Male Scientist 100
5 Anna 39 Female Doctor 250
DataFrame df1
id Name Age Gender Job Income
67 Guto 35 Male Engineer 100
7 Anna 39 Female Doctor 300
9 Melissa 26 Female Student 36
DataFrame df2
id Name Age Gender Job Income
77 Patricia 30 Female Dentist 300
9 Melissa 27 Female Dentist 250
Note that the id (social security number) identifies exactly the person. For instance, the same "Melissa" arises in two different DataFrames. However, there are two different "Annas".
In these dataFrames the number of people and the people vary over time. Some people is represented in all dates and others are represented only in a specific date of time.
Is there a simple way to transform this dictionary of data frames in an (unbalanced) Panel object, where the ids arise in all dates and if the data a given id is not available it is replaced by NaN?
Off course, I can do that making a list of all ids and then checking in each date if a given id is represented. If it is represented, then I copy the data. Otherwise, I just write NaN.
I wonder if there an easy way using pandas tools.
I would recommend using a MultiIndex instead of a Panel.
First, add the period to each dataframe:
for n, df in dictDF.iteritems():
df['period'] = n
Then concatenate into a big dataframe:
big_df = pd.concat([df for df in dictDF.itervalues()], ignore_index=True)
Now set your index to period and id and you are guaranteed to have a unique index:
>>> big_df.set_index(['period', 'id'])
Name Age Gender Job Income
period id
0 10 Daniel 40 Male Scientist 100
5 Anna 39 Female Doctor 250
1 67 Guto 35 Male Engineer 100
7 Anna 39 Female Doctor 300
9 Melissa 26 Female Student 36
2 77 Patricia 30 Female Dentist 300
9 Melissa 27 Female Dentist 250
You can also reverse that order:
>>> big_df.set_index(['id', 'period']).sort_index()
Name Age Gender Job Income
id period
5 0 Anna 39 Female Doctor 250
7 1 Anna 39 Female Doctor 300
9 1 Melissa 26 Female Student 36
2 Melissa 27 Female Dentist 250
10 0 Daniel 40 Male Scientist 100
67 1 Guto 35 Male Engineer 100
77 2 Patricia 30 Female Dentist 300
You can even unstack the data quite easily:
big_df.set_index(['id', 'period'])[['Income']].unstack('period')
Income
period 0 1 2
id
5 250 NaN NaN
7 NaN 300 NaN
9 NaN 36 250
10 100 NaN NaN
67 NaN 100 NaN
77 NaN NaN 300