i have a pandas dataframe with an id column and relatively large text in other column. i want to group by id column and concatenate all the large texts into one single text whenever id repeats. it works great in simple toy example but when i run it on my real data it adds index of rows added in final concatenated text. here is my example code
data = {"A":[1,2,2,3],"B":['asdsa','manish','shukla','wfs']}
testdf = pd.DataFrame(data)
testdf = testdf.groupby(['A'],as_index=False).agg({'B':" ".join})
as you can see this code works great but when i run it on my real data it adds indexes in begnning of column B like it will say something like "1 manish \n 2 shukla" for A=2. it obviously is working here but no idea why its misbehaving when i have larger text with real data. any pointers? i tried to search but apparently noone else has run into this issue.
ok i figured out the answer. if any rows in the dataframe as na or nulls, it does that. once i removed the na and nulls it worked.
Related
My code currently looks like this:
df1 = pd.DataFrame(statsTableList)
df2 = pd.read_csv('StatTracker.csv')
result = pd.concat([df1,df2]).drop_duplicates().reset_index(drop=True)
I get an error and I'm not sure why.
The goal of my program is to pull data from an API, and then write it all to a file for analyzing. df1 is the lets say the first 100 games written to the csv file as the first version. df2 is me reading back those first 100 games the second time around and comparing it to that of df1 (new data, next 100 games) to check for duplicates and delete them.
The part that is not working is the drop duplicates part. It gives me an error of unhashable list, I would assume that's because its two dataframes that are lists of dictionaries. The goal is to pull 100 games of data, and then pull the next 50, but if I pull number 100 again, to drop that one, and just add 101-150 and then add it all to my csv file. Then if I run it again, to pull 150-200, but drop 150 if its a duplicate, etc etc..
Based from your explanation, you can use this one liner to find unique values in df1:
df_diff = df1[~df1.apply(tuple,1)\
.isin(df2.apply(tuple,1))]
This code checks if the rows is exists in another dataframe. To do the comparision it converts each row to tuple (apply tuple conversion along 1 (row) axis).
This solution is indeed slow because its compares each row inside df1 to all rows in df2. So it has time complexity n^2.
If you want more optimised version, try to use pandas built in compare method
df1.compare(df2)
Background info
I'm working on a DataFrame where I have successfully joined two different datasets of football players using fuzzymatcher. These datasets did not have keys for an exact match and instead had to be done by their names. An example match of the name column from two databases to merge as one is the following
long_name name
L. Messi Lionel Andrés Messi Cuccittini
As part of the validation process of a 18,000 row database, I want to check the two date of birth columns in the merged DataFrame - df, ensuring that the columns match like the example below
dob birth_date
1987-06-24 1987-06-24
Both date columns have been converted from strings to dates using pd.to_datetime(), e.g.
df['birth_date'] = pd.to_datetime(df['birth_date'])
My question
My query, I have another column called 'value'. I want to update my pandas DataFrame so that if the two date columns match, the entry is unchanged. However, if the two date columns don't match, I want the data in this value column to be changed to null. This is something I can do quite easily in Excel with a date_diff calculation but I'm unsure in pandas.
My current code is the following:
df.loc[(df['birth_date'] != df['dob']),'value'] = np.nan
Reason for this step (feel free to skip)
The reason for this code is that it will quickly show me fuzzy matches that are inaccurate (approx 10% of total database) and allow me to quickly fix those.
Ideally I need to also work on the matching algorithm to ensure a perfect date match, however, my current algorithm currently works quite well in it's current state and the project is nearly complete. Any advice on this however I'd be happy to hear, if this is something you know about
Many thanks in advance!
IICU:
Please Try np.where.
Works as follows;
np.where(if condition, assign x, else assign y)
if condition=df.loc[(df['birth_date'] != df['dob'],
x=np.nan and
y= prevailing df.value
df['value']= np.where(df.loc[(df['birth_date'] != df['dob']),'value'], np.nan, df['value'])
I am reading a .xslx excel file into a pandas dataframe.
Here is what it looks like:
Image Link
Or in text form:
1 2 3 4
3.5 15.48403728 23.22605592 30.96807456 38.7100932
4 17.41954194 26.12931291 34.83908388 43.54885485
4.5 19.3550466 29.0325699 38.7100932 48.3876165
5 21.29055126 31.93582689 42.58110252 53.22637815
As you can see there is a space in the top left hand cell that is empty.
The rows are amounts and the columns are material, the values are the prices.
I really don't know how to give names properly for indexing.
If I was to try
df.columns = ['Material 1',...'Material 4']
It errors because obviously it is wanting 5 column headers as there are five columns.
Really what I want is to label the top left column as amount/material or something like that, but I don't have a clue on how to do it.
I think the best way would be for me to try and transform this dataframe into something like this:
Amount Material Price
3.5 1 15.48...
3.5 2 23.22...
...
5 4 53.22...
as this will hopefully make it easier to deal with.
Any idea how to do this?
I believe this is called unpivot columns in excel or something like that????
I am not sure how you have read the excel file but if all you wanted is to rename your columns then you can set column names while reading the excel itself.
Supposing my file name is MyExcelFile.xlsx and the columns names that are there 'Amount','Material_1','Material_2','Material_3' and 'Material_4' then I will read it as follows. If these column names do not exist (in the excel) then you have to pass header=None explicitly.
MyDF = pd.read_excel('/FullPathToYourExcelFile/MyExcelFile.xlsx', names=['Amount','Material_1','Material_2','Material_3','Material_4'], header=None)
The output is as below.
See the documentation here (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html). If you have already done it, as I have suggested above, then I am sorry I have underestimated your problem requirements. All the best
I'm organizing a new dataframe in order to easily insert data into a Bokeh visualization code snippet. I think my problem is due to differing row lengths, but I am not sure.
Below, I organized the dataset in alphabetical order, by country name, and created an alphabetical list of the individual countries. new_data.tail() Although Zimbabwe is listed last, there are 80336 rows, hence the sorting.
df_ind_data = pd.DataFrame(ind_data)
new_data = df_ind_data.sort_values(by=['country'])
new_data = new_data.reset_index(drop=True)
country_list = list(ind_data['country'])
new_country_set = sorted(set(country_list))
My goal is create a new DataFrame, with 76 cols (country names), with the specific 'trust' data in the rows underneath each country column.
df = pd.DataFrame()
for country in new_country_set:
pink = new_data.loc[(new_data['country'] == country)]
df[country] = pink.trust
Output here
As you can see, the data does not get included for the rest of the columns after the first. I believe this is due to the fact that the number of rows of 'trust' data for each country varies. While the first column has 1000 rows, there are some with as many as 2500 data points, and as little as 500.
I have attempted a few different methods to specify the number of rows in 'df', but to no avail.
The visualization code snippet I have utilizes this same exact data structure for the template data, so that it why I'm attempting to put it in a dataframe. Plus, I can't do it, so I want to know how to do it.
Yes, I can put it in a dictionary, but I want to put it in a dataframe.
You should use combine_first when you add a new column so that the dataframe index gets extended. Instead of
df[country] = pink.trust
you should use
df = pink.trust.combine_first(df)
which ensures that your index is always union of all added columns.
I think in this case pd.pivot(columns = 'var', values = 'val') , will work for you, especially when you already have dataframe. This function will transfer values from particular column into column names. You could see the documentation for additional info. I hope that helps.
To preface: I'm new to using Python.
I'm working on cleaning up a file where data was spread across multiple rows. I'm struggling to find a solution that will concatenate multiple text strings to a single cell. The .csv data looks similar to this:
name,date,description
bundy,12-12-2017,good dog
,,smells kind of weird
,,needs to be washed
with one or two blank rows between each entry, too.
The amount of rows used for 'description' isn't consistent. Sometimes it's just one cell, sometimes up to about four. The ideal output turns these multiple rows into a single row of useful data, without all the wasted space. I thought maybe I could create a series of masks by copying the data across a few columns, shifted up, and then iterating in some way. I haven't found a solution that matches what I'm trying to do, though. This is where I'm at so far:
#Add column f description stuff and shift up a row for concatenation
DogData['Z'] = DogData['Y'].shift(-1)
DogData['AA'] = DogData['Z'].shift(-1)
DogData['AB'] = DogData['AA'].shift(-1)
#create series checks to determine how to concat values properly
YNAs = DogData['Y'].isnull()
ZNAs = DogData['Z'].isnull()
AANAs = DogData['AA'].isnull()
The idea here was basically that I'd iterate over column 'Y', check if the same row in column 'Z' was NA or had a value, and concat if it did. If not, just use the value in 'Y'. Carry that logic across but stopping if it encountered an NA in any subsequent columns. I can't figure out how to do that, or if there's a more efficient way to do this.
What do I have to do to get to my end result? I can't figure out the right way to iterate or concatenate in the way I was hoping to.
'''
name,date,description
bundy,12-12-2017,good dog
,,smells kind of weird
,,needs to be washed
'''
df = pd.read_clipboard(sep=',')
df.fillna(method = 'ffill').groupby([
'name',
'date'
]).description.apply(lambda x : ', '.join(x)).to_frame(name = 'description')
I'm not sure I follow exactly what you mean. I took that text, saved it as a csv file, and successfully read it into a pandas dataframe.
import pandas as pd
df = pd.read_csv('test.csv')
df
Output:
name date description
0 bundy 12-12-2017 good dog
1 NaN NaN smells kind of weird
2 NaN NaN needs to be washed
Isn't this the output you require?