So I have a dataframe with different data:
Ordernumber
Name
customerID
Abel
1939184849
Rose
1029480129
Rob
1283949203
As you can see the name and customerID are already filled in.
Now I need to generate unique ordernumbers, please keep in mind that the actual database has around 20,000 rows, so the ordernumber has to be unique.
Can it be only numbers? i.e. ranging from 0 to the total length of your dataframe?
df['Ordernumber'] = [n for n in range(0,len(df),1)]
Related
Could anyone help me ?
I want to sum the values with the format:
print (...+....+)
for example:
a b
France 2
Italie 15
Croatie 7
I want to make the sum of France and Croatie.
Thank you for your help !
One of possible solutions:
set column a as the index,
using loc select rows for the "wanted" values,
take column b,
sum the values found.
So the code can be:
result = df.set_index('a').loc[['France', 'Croatie']].b.sum()
Note double square brackets. The outer pair is the "container" of index values
passed to loc.
The inner part, and what is inside, is a list of values.
To subtract two sums (one for some set of countries and the second for another set),
you can run e.g.:
wrk = df.set_index('a').b
result = wrk.loc[['Italie', 'USA']].sum() - wrk.loc[['France', 'Croatie']].sum()
I got a df of more than 13000 of rows with more than 154 columns. I have a column: 'caseid' with a value of: 2298 and i want to print out that row with the value of other column with the name of 'prglngth'. The value that i looking for is in the key: 'prglngth'.
My steps were: first: find the index of the row of the value 2298 of the 'caseid' column.
second: then try to match with the column: 'prglngth' to find the value of this column, and i already lost 48hs trying it. Any help will be appreciated!!
Try to use:
df.loc[df['caseid'] == 2298, 'prglngth']
Created dictionary called items. Combined values for different keys and put into variable food_list.
items={'Food':['Ice Cream','Salad'],'Computer':['Laptop','Notebook']
food_list= '|'.join(items['Food'])
Description Amount
Lenovo Laptop 300
Chicken Salad 40
Dell Notebook 250
Chocolate Ice Cream 3
I tried to find a string based on dictionary values. If the string is in the dictionary, then the row in the dataframe contains the string. I take the amount it is associated with and add up the total row amounts that fit the condition.
total_amount=df.loc[df['Description'].str.contains(food_list,na=False)
==df['Amount'].sum()]
I usually run the code and get
Empty DataFrame
Columns: [Date, Description, Amount]
Index: []
Help with homework problem: "Let us define the "data science experience" of a given person as the person's largest score among Regression, Classification, and Clustering. Compute the average data science experience among all MSIS students."
Beginner to coding. I am trying to figure out how to check amongst columns and compare those columns to each other for the largest value. And then take the average of those found values.
I greatly appreciate your help in advance!
Picture of the sample data set: 1: https://i.stack.imgur.com/9OSjz.png
Provided Code:
import pandas as pd
df = pd.read_csv("cleaned_survey.csv", index_col=0)
df.drop(['ProgSkills','Languages','Expert'],axis=1,inplace=True)
Sample Data:
What I have tried so far:
df[data_science_experience]=df[["Regression","Classification","Clustering"]].values.max()
df['z']=df[['Regression','Classification','Clustering']].apply(np.max,axis=1)
df[data_science_experience]=df[["Regression","Classification","Clustering"]].apply(np.max,axis=1)
If you want to get the highest score of column 'hw1' you can get it with:
pd['hw1'].max(). this gives you a series of all the values in that column and max returns the maximum. for average use mean:
pd['hw1'].mean()
if you want to find the maximum of multiple columns, you can use:
maximum_list = list()
for col in pd.columns:
maximum_list.append(pd[col].max)
max = maximum_list.max()
avg = maximum_list.mean()
hope this helps.
First, you want to get only the rows with MSIS in the Program column. That can be done in the following way:
df[df['Program'] == 'MSIS']
Next, you want to get only the Regression, Classification and Clustering columns. The previous query filtered only rows; we can add to that, like this:
df.loc[df['Program'] == 'MSIS', ['Regression', 'Classification', 'Clustering']]
Now, for each row remaining, we want to take the maximum. That can be done by appending .max(axis=1) to the previous line (axis=1 because we want the maximum of each row, not each column).
At this point, we should have a DataFrame where each row represents the highest score of the three categories for each student. Now, all that's left to do is take the mean, which can be done with .mean(). The full code should therefore look like this:
df.loc[df['Program'] == 'MSIS', ['Regression', 'Classification', 'Clustering']].max(axis=1).mean()
I have a DataFrame with the following structure.
df = pd.DataFrame({'tenant_id': [1,1,1,2,2,2,3,3,7,7], 'user_id': ['ab1', 'avc1', 'bc2', 'iuyt', 'fvg', 'fbh', 'bcv', 'bcb', 'yth', 'ytn'],
'text':['apple', 'ball', 'card', 'toy', 'sleep', 'happy', 'sad', 'be', 'u', 'pop']})
This gives the following output:
df = df[['tenant_id', 'user_id', 'text']]
tenant_id user_id text
1 ab1 apple
1 avc1 ball
1 bc2 card
2 iuyt toy
2 fvg sleep
2 fbh happy
3 bcv sad
3 bcb be
7 yth u
7 ytn pop
I would like to groupby on tenant_id and create a new column which is a random selection of strings from the user_id column.
Thus, I would like my output to look like the following:
tenant_id user_id text new_column
1 ab1 apple [ab1, bc2]
1 avc1 ball [ab1]
1 bc2 card [avc1]
2 iuyt toy [fvg, fbh]
2 fvg sleep [fbh]
2 fbh happy [fvg]
3 bcv sad [bcb]
3 bcb be [bcv]
7 yth u [pop]
7 ytn pop [u]
Here, random id's from the user_id column have been selected, these id's can be repeated as "fvg" is repeated for tenant_id=2. I would like to have a threshold of not more than ten id's. This data is just a sample and has only 10 id's to start with, so generally any number much less than the total number of user_id's. This case say 1 less than total user_id's that belong to a tenant.
i tried first figuring out how to select random subset of varying length with
df.sample
new_column = df.user_id.sample(n=np.random.randint(1, 10)))
I am kinda lost after this, assigning it to my df results in Nan's, probably because they are of variable lengths. Please help.
Thanks.
per my comment:
Your 'new column' is not a new column, it's a new cell for a single row.
If you want to assign the result to a new column, you need to create a new column, and apply the cell computation to it.
df['new column'] = df['user_id'].apply(lambda x: df.user_id.sample(n=np.random.randint(1, 10))))
it doesn't really matter what column you use for the apply since the variable is not used in the computation