I do this:
join = lambda x: ' '.join(x)
df_temp = df.groupby(['Id']).agg({'InfoType': join,'InfoLabel1': join, 'InfoLabel2': join})
and it works.
Is there any other more efficient way (in terms of code lines etc) to do this?
For example I did this:
df_temp = df.groupby(['Id'])['InfoType', 'InfoLabel1', 'InfoLabel2'].agg(lambda x: ', '.join(x))
but this outputs only the Id and InfoType columns.
P.S.
Hm it seems that now also this is working:
df_temp = df.groupby(['Id'])['InfoType', 'InfoLabel1', 'InfoLabel2'].agg(lambda x: ', '.join(x))
This happened after I converted all columns to strings - the columns contained NAs too.
I am having the impression that pandas was encountering some errors that it was not raising and it was just outputing the columns (i.e. 'InfoType') at which it did not encounter the errors).
Related
I have a data frame with rows that are mostly translations of other rows e.g. an English row and an Arabic row. They share an identifier (location_shelfLocator) and I'm trying to merge the rows together based on the identifier match. In some columns the Arabic doesn't contain a translation, but the same English value (e.g. for the language column both records might have ['ger'] which becomes ['ger', 'ger']) so I would like to get rid of these duplicate values. This is my code:
df_merged = df_filled.groupby("location_shelfLocator").agg(
lambda x: np.unique(x.tolist())
)
It works when the values being aggregated are the same type (e.g. when they are both strings or when they are both arrays). When one is a string and the other is an array, it doesn't work. I get this warning:
FutureWarning: ['subject_name_namePart'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.
df_merged = df_filled.groupby("location_shelfLocator").agg(lambda x: np.unique(x.tolist()))
and the offending column is removed from the final data frame. Any idea how I can combine these values and remove duplicates when they are both lists, both strings, or one of each?
Here is some sample data:
location_shelfLocator,language_languageTerm,subject_topic,accessCondition,subject_name_namePart
81055/vdc_100000000094.0x000093,ara,"['فلك، العرب', 'فلك، اليونان', 'فلك، العصور الوسطى', 'الكواكب']",المُلكية العامة,كلاوديوس بطلميوس (بطليمو)
81055/vdc_100000000094.0x000093,ara,"['Astronomy, Arab', 'Astronomy, Greek', 'Astronomy, Medieval', 'Constellations']",Public Domain,"['Claudius Ptolemaeus (Ptolemy)', ""'Abd al-Raḥmān ibn 'Umar Ṣūfī""]"
And expected output:
location_shelfLocator,language_languageTerm,subject_topic,accessCondition,subject_name_namePart
"[‘81055/vdc_100000000094.0x000093’] ",[‘ara’],"['فلك، العرب', 'فلك، اليونان', 'فلك، العصور الوسطى', ‘الكواكب’, 'Astronomy, Arab', 'Astronomy, Greek', 'Astronomy, Medieval', 'Constellations']","[‘المُلكية العامة’, ‘Public Domain’]","[‘كلاوديوس بطلميوس (بطليمو)’,’Claudius Ptolemaeus (Ptolemy)', ""'Abd al-Raḥmān ibn 'Umar Ṣūfī""]"
If you cannot have a control over the input value, you need to fix it somehow.
Something like this. Here, I am converting string value in subject_name_namePart to array of string.
from ast import literal_eval
mask = df.subject_name_namePart.str[0] != '['
df.loc[mask, 'subject_name_namePart'] = "['" + df.loc[mask, 'subject_name_namePart'] + "']"
df['subject_name_namePart'] = df.subject_name_namePart.transform(literal_eval)
Then, you can do (explode) + aggregation.
df = df.explode('subject_name_namePart')
df = df.groupby('location_shelfLocator').agg(lambda x: x.unique().tolist())
I need to fill a pandas dataframe in a list comprehension.
Although rows satisfying the criterias are appended to the dataframe.
However, at the end, dataframe is empty.
Is there a way to resolve this?
In real code, I'm doing many other calculations. This is a simplified code to regenerate it.
import pandas as pd
main_df = pd.DataFrame(columns=['a','b','c','d'])
main_df=main_df.append({'a':'a1', 'b':'b1','c':'c1', 'd':'d1'},ignore_index=True)
main_df=main_df.append({'a':'a2', 'b':'b2','c':'c2', 'd':'d2'},ignore_index=True)
main_df=main_df.append({'a':'a3', 'b':'b3','c':'c3', 'd':'d3'},ignore_index=True)
main_df=main_df.append({'a':'a4', 'b':'b4','c':'c4', 'd':'d4'},ignore_index=True)
print(main_df)
sub_df = pd.DataFrame()
df_columns = main_df.columns.values
def search_using_list_comprehension(row,sub_df,df_columns):
if row[0]=='a1' or row[0]=='a2':
dict= {a:b for a,b in zip(df_columns,row)}
print('dict: ', dict)
sub_df=sub_df.append(dict, ignore_index=True)
print('sub_df.shape: ', sub_df.shape)
[search_using_list_comprehension(row,sub_df,df_columns) for row in main_df.values]
print(sub_df)
print(sub_df.shape)
The problem is that you define an empty frame with sub_df = dp.DataFrame() then you assign the same variable within the function parameters and within the list comprehension you provide always the same, empty sub_df as parameter (which is always empty). The one you append to within the function is local to the function only. Another “issue” is using python’s dict variable as user defined. Don’t do this.
Here is what can be changed in your code in order to work, but I would strongly advice against it
import pandas as pd
df_columns = main_df.columns.values
sub_df = pd.DataFrame(columns=df_columns)
def search_using_list_comprehension(row):
global sub_df
if row[0]=='a1' or row[0]=='a2':
my_dict= {a:b for a,b in zip(df_columns,row)}
print('dict: ', my_dict)
sub_df = sub_df.append(my_dict, ignore_index=True)
print('sub_df.shape: ', sub_df)
[search_using_list_comprehension(row) for row in main_df.values]
print(sub_df)
print(sub_df.shape)
I have a data frame which is conditionally broken up into two separate dataframes as follows:
df = pd.read_csv(file, names)
df = df.loc[df['name1'] == common_val]
df1 = df.loc[df['name2'] == target1]
df2 = df.loc[df['name2'] == target2]
# each df has a 'name3' I want to perform a division on after this filtering
The original df is filtered by a value shared by the two dataframes, and then each of the two new dataframes are further filtered by another shared column.
What I want to work:
df1['name3'] = df1['name3']/df2['name3']
However, as many questions have pointed out, this causes a setting with copy warning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
I tried what was recommended in this question:
df1.loc[:,'name3'] = df1.loc[:,'name3'] / df2.loc[:,'name3']
# also tried:
df1.loc[:,'name3'] = df1.loc[:,'name3'] / df2['name3']
But in both cases I still get weird behavior and the set by copy warning.
I then tried what was recommended in this answer:
df.loc[df['name2']==target1, 'name3'] = df.loc[df['name2']==target1, 'name3']/df.loc[df['name2'] == target2, 'name3']
which still results in the same copy warning.
If possible I would like to avoid copying the data frame to get around this because of the size of these dataframes (and I'm already somewhat wastefully making two almost identical dfs from the original).
If copying is the best way to go with this problem I'm interested to hear why that works over all the options I explored above.
Edit: here is a simple data frame along the lines of what df would look like after the line df.loc[df['name1'] == common_val]
name1 other1 other2 name2 name3
a x y 1 2
a x y 1 4
a x y 2 5
a x y 2 3
So if target1=1 and target2=2,
I would like df1 to contain only rows where name1=1 and df2 to contain only rows where name2=2, then divide the resulting df1['name3'] by the resulting df2['name3'].
If there is a less convoluted way to do this (without splitting the original df) I'm open to that as well!
I'm trying to understand how to concat three individual dataframes (i.e df1, df2, df3) into a new dataframe say df4 whereby each individual dataframe has its own column left to right order.
I've tried using concat with axis = 1 to do this, but it appears not possible to automate this with a single action.
Table1_updated = pd.DataFrame(columns=['3P','2PG-3Io','3Io'])
Table1_updated=pd.concat([get_table1_3P,get_table1_2P_max_3Io,get_table1_3Io])
Note that with the exception of get_table1_2P_max_3Io, which has two columns, all other dataframes have one column
For example,
get_table1_3P =
get_table1_2P_max_3Io =
get_table1_3Io =
Ultimately, i would like to see the following:
I believe you need first concat and tthen change order by list of columns names:
Table1_updated=pd.concat([get_table1_3P,get_table1_2P_max_3Io,get_table1_3Io], axis=1)
Table1_updated = Table1_updated[['3P','2PG-3Io','3Io']]
I am facing issues trying to select a subset of columns and running unique on it.
Source Data:
df_raw = pd.read_csv('data/master.csv', nrows=10000)
df_raw.shape()
Produces:
(10000, 86)
Process Data:
df = df_raw[['A','B','C']]
df.shape()
Produces:
(10000, 3)
Furthermore, doing:
df_raw.head()
df.head()
produces a correct list of rows and columns.
However,
print('RAW:',sorted(df_raw['A'].unique()))
works perfectly
Whilst:
print('PROCESSED:',sorted(df['A'].unique()))
produces:
AttributeError: 'DataFrame' object has no attribute 'unique'
What am I doing wrong? If the shape and head output are exactly what I want, I'm confused why my processed dataset is throwing errors. I did read Pandas 'DataFrame' object has no attribute 'unique' on SO which correctly states that unique needs to be applied to columns which is what I am doing.
This was a case of a duplicate column. Given this is proprietary data, I abstracted it as 'A', 'B', 'C' in this question and therefore masked the problem. (The real data set had 86 columns and I had duplicated one of those columns twice in my subset, and was trying to do a unique on that)
My problem was this:
df_raw = pd.read_csv('data/master.csv', nrows=10000)
df = df_raw[['A','B','C', 'A']] # <-- I did not realize I had duplicated A later.
This was causing problems when doing a unique on 'A'
From the entire dataframe to extract a subset a data based on a column ID. This works!!
df = df.drop_duplicates(subset=['Id']) #where 'id' is the column used to filter
print (df)