I was comparing two excel files which contains the information of the students of two schools. However those files might contain different number of rows between them.
The first set that I used is to import the excel files in two dataframes:
df1 = pd.read_excel('School A - Information.xlsx')
df2 = pd.read_excel('School B - Information.xlsx')
print(df1)
Name Age Birth_Country Previous Schools
0 tom 10 USA 3
1 nick 15 MEX 1
2 juli 14 CAN 0
3 tom 19 NOR 1
print(df2)
Name Age Birth_Country Previous Schools
0 tom 10 USA 3
1 tom 19 NOR 1
2 nick 15 MEX 4
After this, I would like to check the divergences between those two dataframes (index order is not important). However I am receiving an error due to the size of the dataframes.
compare = df1.values == df2.values
<ipython-input-9-7cc64ba0e622>:1: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
compare = df1.values == df2.values
print(compare)
False
Adding to that, I would like to create a third DataFrame with the corresponding divergences, that shows the divergence.
import numpy as np
rows,cols=np.where(compare==False)
for item in zip(rows,cols):
df1.iloc[item[0], item[1]] = '{} --> {}'.format(df1.iloc[item[0], item[1]],df2.iloc[item[0], item[1]])
However, using this code is not working, as the index order may be different between the two dataframes.
My expected output should be the below dataframe:
You can use pd.merge to accomplish this. If you're unfamiliar with dataframe merges, here's a post that describes relational database merging ideas: link. So in this case, what we want to do is first do a left merge of df2 onto df1 to find how the Previous Schools column differs:
df_merged = pd.merge(df1, df2, how="left", on=["Name", "Age", "Birth_Country"], suffixes=["_A", "_B"])
print(df_merged)
will give you a new dataframe
Name Age Birth_Country Previous Schools_A Previous Schools_B
0 tom 10 USA 3 3.0
1 nick 15 MEX 1 4.0
2 juli 14 CAN 0 NaN
3 tom 19 NOR 1 1.0
This new dataframe has all the information you're looking for. To find just the rows where the Previous Schools entries differ:
df_different = df_merged[df_merged["Previous Schools_A"]!=df_merged["Previous Schools_B"]]
print(df_different)
Name Age Birth_Country Previous Schools_A Previous Schools_B
1 nick 15 MEX 1 4.0
2 juli 14 CAN 0 NaN
and to find the rows where Previous Schools has not changed:
df_unchanged = df_merged[df_merged["Previous Schools_A"]==df_merged["Previous Schools_B"]]
print(df_unchanged)
Name Age Birth_Country Previous Schools_A Previous Schools_B
0 tom 10 USA 3 3.0
3 tom 19 NOR 1 1.0
If I were you, I'd stop here, because creating the final dataframe you want is going to have generic object column types because of the mix of strings and integers, which will limit its uses... but maybe you need it in that particular formattting for some reason. In which case, it's all about putting together these dataframe subsets in the right way to get your desired formatting. Here's one way.
First, initialize the final dataframe with the unchanged rows:
df_final = df_unchanged[["Name", "Age", "Birth_Country", "Previous Schools_A"]].copy()
df_final = df_final.rename(columns={"Previous Schools_A": "Previous Schools"})
print(df_final)
Name Age Birth_Country Previous Schools
0 tom 10 USA 3
3 tom 19 NOR 1
now process the entries that have changed between dataframes. There are two cases here: where the entries have changed (where Previous Schools_B is not NaN) and where the entrie is new (where Previous Schools_B is NaN). We'll deal with each in turn:
changed_entries = df_different[~pd.isnull(df_different["Previous Schools_B"])].copy()
changed_entries["Previous Schools"] = changed_entries["Previous Schools_A"].astype('str') + " --> " + changed_entries["Previous Schools_B"].astype('int').astype('str')
changed_entries = changed_entries.drop(columns=["Previous Schools_A", "Previous Schools_B"])
print(changed_entries)
Name Age Birth_Country Previous Schools
1 nick 15 MEX 1 --> 4
and now process the entries that are completely new:
new_entries = df_different[pd.isnull(df_different["Previous Schools_B"])].copy()
new_entries = "NaN --> " + new_entries[["Name", "Age", "Birth_Country", "Previous Schools_A"]].astype('str')
new_entries = new_entries.rename(columns={"Previous Schools_A": "Previous Schools"})
print(new_entries)
Name Age Birth_Country Previous Schools
2 NaN --> juli NaN --> 14 NaN --> CAN NaN --> 0
and finally, concatenate all the dataframes:
df_final = pd.concat([df_final, changed_entries, new_entries])
print(df_final)
Name Age Birth_Country Previous Schools
0 tom 10 USA 3
3 tom 19 NOR 1
1 nick 15 MEX 1 --> 4
2 NaN --> juli NaN --> 14 NaN --> CAN NaN --> 0
Related
From the dataframe, I create a new dataframe, in which the values from the "Select activity" column contain lists, which I will split and transform into new rows. But there is a value: "Nothing, just walking", which I need to leave unchanged. Tell me, please, how can I do this?
The original dataframe looks like this:
Name Age Select activity Profession
0 Ann 25 Cycling, Running Saleswoman
1 Mark 30 Nothing, just walking Manager
2 John 41 Cycling, Running, Swimming Accountant
My code looks like this:
df_new = df.loc[:, ['Name', 'Age']]
df_new['Activity'] = df['Select activity'].str.split(', ')
df_new = df_new.explode('Activity').reset_index(drop=True)
I get this result:
Name Age Activity
0 Ann 25 Cycling
1 Ann 25 Running
2 Mark 30 Nothing
3 Mark 30 just walking
4 John 41 Cycling
5 John 41 Running
6 John 41 Swimming
In order for the value "Nothing, just walking" not to be divided by 2 values, I added the following line:
if df['Select activity'].isin(['Nothing, just walking']) is False:
But it throws an error.
then let's look ahead after comma to guarantee a Capital letter, and only then split. So instead of , we have , (?=[A-Z])
df_new = df.loc[:, ["Name", "Age"]]
df_new["Activity"] = df["Select activity"].str.split(", (?=[A-Z])")
df_new = df_new.explode("Activity", ignore_index=True)
i only changed the splitter, and ignore_index=True to explode instead of resetting afterwards (also the single quotes..)
to get
>>> df_new
Name Age Activity
0 Ann 25 Cycling
1 Ann 25 Running
2 Mark 30 Nothing, just walking
3 John 41 Cycling
4 John 41 Running
5 John 41 Swimming
one line as usual
df_new = (df.loc[:, ["Name", "Age"]]
.assign(Activity=df["Select activity"].str.split(", (?=[A-Z])"))
.explode("Activity", ignore_index=True))
DOB Name
0 1956-10-30 Anna
1 1993-03-21 Jerry
2 2001-09-09 Peter
3 1993-01-15 Anna
4 1999-05-02 James
5 1962-12-17 Jerry
6 1972-05-04 Kate
In the dataframe similar to the one above where I have duplicate names. So I am want to add a suffix '_0' to the name if DOB is before 1990 and a duplicate name.
I am expecting a result like this
DOB Name
0 1956-10-30 Anna_0
1 1993-03-21 Jerry
2 2001-09-09 Peter
3 1993-01-15 Anna
4 1999-05-02 James
5 1962-12-17 Jerry_0
6 1972-05-04 Kate
I am using the following
df['Name'] = df[(df['DOB'] < '01-01-1990') & (df['Name'].isin(['Anna','Jerry']))].Name.apply(lambda x: x+'_0')
But I am getting this result
DOB Name
0 1956-10-30 Anna_0
1 1993-03-21 NaN
2 2001-09-09 NaN
3 1993-01-15 NaN
4 1999-05-02 NaN
5 1962-12-17 Jerry_0
6 1972-05-04 NaN
How can I add a suffix to the Name which is a duplicate and have to be born before 1990.
Problem in your df['Name'] = df[(df['DOB'] < '01-01-1990') & (df['Name'].isin(['Anna','Jerry']))].Name.apply(lambda x: x+'_0') is that df[(df['DOB'] < '01-01-1990') & (df['Name'].isin(['Anna','Jerry']))] is a filtered dataframe whose rows are less than the original. When you assign it back, the not filtered rows doesn't have corresponding value in the filtered dataframe, so it becomes NaN.
You can try mask instead
m = (df['DOB'] < '1990-01-01') & df['Name'].duplicated(keep=False)
df['Name'] = df['Name'].mask(m, df['Name']+'_0')
You can use masks and boolean indexing:
# is the year before 1990?
m1 = pd.to_datetime(df['DOB']).dt.year.lt(1990)
# is the name duplicated?
m2 = df['Name'].duplicated(keep=False)
# if both conditions are True, add '_0' to the name
df.loc[m1&m2, 'Name'] += '_0'
output:
DOB Name
0 1956-10-30 Anna_0
1 1993-03-21 Jerry
2 2001-09-09 Peter
3 1993-01-15 Anna
4 1999-05-02 James
5 1962-12-17 Jerry_0
6 1972-05-04 Kate
I have a DataFrame and want to extract 3 columns from it, but one of them is an input from the user. I made a list, but need it to be iterable so I can run a For iteration.
So far I made it through by making a dictionary with 2 of the columns making a list of each and zipping them... but I really need the 3 columns...
My code:
Data=pd.read_csv(----------)
selec=input("What month would you want to show?")
NewData=[(Data['Country']),(Data['City']),(Data[selec].astype('int64')]
#here I try to iterate:
iteration=[i for i in NewData if NewData[i]<=25]
print (iteration)
*TypeError:list indices must be int ot slices, not Series*
My CSV is the following:
I want to be able to choose the month with the variable "selec" and filter the results of the month I've chosen... so the output for selec="Feb" would be:
I tried as well with loc/iloc, but not lucky at all (unhashable type:'list').
See the below example for how you can:
select specific columns from a DataFrame by providing a list of columns between the selection brackets (link to tutorial)
select specific rows from a DataFrame by providing a condition between the selection brackets (link to tutorial)
iterate rows of a DataFrame, although I don't suppose you need it - if you'd like to keep working with the DataFrame after filtering it, it's better to use the method mentioned above (you won't have to put the rows back together, and it will likely be more performant because pandas is optimized for bulk operations)
import pandas as pd
# this is just for testing, instead of pd.read_csv(...)
df = pd.DataFrame([
dict(Country="Spain", City="Madrid", Jan="15", Feb="16", Mar="17", Apr="18", May=""),
dict(Country="Spain", City="Galicia", Jan="1", Feb="2", Mar="3", Apr="4", May=""),
dict(Country="France", City="Paris", Jan="0", Feb="2", Mar="3", Apr="4", May=""),
dict(Country="Algeria", City="Argel", Jan="20", Feb="28", Mar="29", Apr="30", May=""),
])
print("---- Original df:")
print(df)
selec = "Feb" # let's pretend this comes from input()
print("\n---- Just the 3 columns:")
df = df[["Country", "City", selec]] # narrow down the df to just the 3 columns
df[selec] = df[selec].astype("int64") # convert the selec column to proper type
print(df)
print("\n---- Filtered dataframe:")
df1 = df[df[selec] <= 25]
print(df1)
print("\n---- Iterated & filtered rows:")
for row in df.itertuples():
# we could also use row[3] instead of getattr(...)
if getattr(row, selec) <= 25:
print(row)
Output:
---- Original df:
Country City Jan Feb Mar Apr May
0 Spain Madrid 15 16 17 18
1 Spain Galicia 1 2 3 4
2 France Paris 0 2 3 4
3 Algeria Argel 20 28 29 30
---- Just the 3 columns:
Country City Feb
0 Spain Madrid 16
1 Spain Galicia 2
2 France Paris 2
3 Algeria Argel 28
---- Filtered dataframe:
Country City Feb
0 Spain Madrid 16
1 Spain Galicia 2
2 France Paris 2
---- Iterated & filtered dataframe:
Pandas(Index=0, Country='Spain', City='Madrid', Feb=16)
Pandas(Index=1, Country='Spain', City='Galicia', Feb=2)
Pandas(Index=2, Country='France', City='Paris', Feb=2)
i'm working on spread r equivalent in pandas my dataframe looks like below
Name age Language year Period
Nik 18 English 2018 Beginer
John 19 French 2019 Intermediate
Kane 33 Russian 2017 Advanced
xi 44 Thai 2015 Beginer
and looking for output like this
Name age Language Beginer Intermediate Advanced
Nik 18 English 2018
John 19 French 2019
Kane 33 Russian 2017
John 44 Thai 2015
my code
pd.pivot(x1,values='year', columns=['Period'])
i'm getting only these columns Beginer,Intermediate,Advanced not the entire dataframe
while reshaping it i tried using index but says no duplicates in index.
So i created new index column but still not getting entire dataframe
If I understood correctly you could do something like this:
# create dummy columns
res = pd.get_dummies(df['Period']).astype(np.int64)
res.values[np.arange(len(res)), np.argmax(res.values, axis=1)] = df['year']
# concat and drop columns
output = pd.concat((df.drop(['year', 'Period'], 1), res), 1)
print(output)
Output
Name age Language Advanced Beginner Intermediate
0 Nik 18 English 0 2018 0
1 John 19 French 0 0 2019
2 Kane 33 Russian 2017 0 0
3 xi 44 Thai 0 2015 0
If you want to match the exact same output, convert the column to categorical first, and specify the order:
# encode as categorical
df['Period'] = pd.Categorical(df['Period'], ['Beginner', 'Advanced', 'Intermediate'], ordered=True)
# create dummy columns
res = pd.get_dummies(df['Period']).astype(np.int64)
res.values[np.arange(len(res)), np.argmax(res.values, axis=1)] = df['year']
# concat and drop columns
output = pd.concat((df.drop(['year', 'Period'], 1), res), 1)
print(output)
Output
Name age Language Beginner Advanced Intermediate
0 Nik 18 English 2018 0 0
1 John 19 French 0 0 2019
2 Kane 33 Russian 0 2017 0
3 xi 44 Thai 2015 0 0
Finally if you want to replace the 0, with missing values, add a third step:
# create dummy columns
res = pd.get_dummies(df['Period']).astype(np.int64)
res.values[np.arange(len(res)), np.argmax(res.values, axis=1)] = df['year']
res = res.replace(0, np.nan)
Output (with missing values)
Name age Language Beginner Advanced Intermediate
0 Nik 18 English 2018.0 NaN NaN
1 John 19 French NaN NaN 2019.0
2 Kane 33 Russian NaN 2017.0 NaN
3 xi 44 Thai 2015.0 NaN NaN
One way you can get to the equivalent of R's spread function using pd.pivot_table:
If you don't mind about the index, you can use reset_index() on the newly created df:
new_df = (pd.pivot_table(df, index=['Name','age','Language'],columns='Period',values='year',aggfunc='sum')).reset_index()
which will get you:
Period Name age Language Advanced Beginer Intermediate
0 John 19 French NaN NaN 2019.0
1 Kane 33 Russian 2017.0 NaN NaN
2 Nik 18 English NaN 2018.0 NaN
3 xi 44 Thai NaN 2015.0 NaN
EDIT
If you have many columns in your dataframe and you want to include them in the reshaped dataset:
Grab in a list the columns to be used in pivot table (i.e. Period and year)
Grab all the other columns in your dataframe in a list (using not in)
Use the index_cols as index in the pd.pivot_table() command
non_index_cols = ['Period','year'] # SPECIFY THE 2 COLUMNS IN THE PIVOT TABLE TO BE USED
index_cols = [i for i in df.columns if i not in non_index_cols] # GET ALL THE REST IN A LIST
new_df = (pd.pivot_table(df, index=index_cols,columns='Period',values='year',aggfunc='sum')).reset_index()
The new_df, will include all the columns of your initial dataframe.
TL;DR:
Have 2 dataframes with different sizes, but one 'id' column(in both df) that supposed to act as index. Need to merge them, group by 'sector' and 'gender' and count/sum entrys in each group.
Long version:
I have a dataframe with 'id', 'sector', among other columns, from company personnel. Another dataframe with 'id' and 'gender'. Examples bellow:
df1:
row* id sector other columns
1 0 Operational ...
2 0 Administrative ...
3 1 Sales ...
4 2 IT ...
5 3 Operational ...
6 3 IT ...
7 4 Sales ...
[...]
150 100 Operational ...
151 100 Sales ...
152 101 IT ...
*I don't really have a 'row' column, it's there just to make it easier to understand my problem.
df2:
row* id gender
1 0 Male
2 1 Female
3 2 Female
4 3 Male
5 4 Male
[...]
101 100 Male
102 101 Female
As you can see, one person can be in more then one sector (which seems to make my problem more complicated.)
I need to merge them together and then make a sum from how many male and female in each sector.
FIRST PROBLEM
Decided to make a new df to get only the columns 'id' and 'sector'.
df3 = df1[['id','sector']]
df3 = df3.merge(df2)
I get:
No common columns to perform merge on. Merge options: left_on=None,
right_on=None, left_index=False, right_index=False
Tried using .join() instead of .merge() and I get:
['id'] not in index"
Tried now with reset_index() - Found in some of the answers around here, but didn't really solved my issue.
df1 = df1.reset_index()
df3 = df1[['id','sector']]
df3 = df3.join(df2)
What I got was this:
row* id sector gender
1 0 Operational Male
2 0 Administrative Female
3 1 Sales Female
4 2 IT Male
5 3 Operational Male
6 3 IT ...
7 4 Sales ...
[...]
150 100 Operational NaN
151 100 Sales NaN
152 101 IT NaN
It didn't respected the 'id' and just concatenated the column to the side. Since df2 only had 102 rows, I got NaN in the other rows(103 to 152), aside from the fact that the 'gender' was no longer accurate.
SECOND PROBLEM
Decided to power through that in order to get the rest of the work done. I tried this:
df3 = df3.groupby('sector','gender').size()
It raises:
No axis named gender for object type < class 'pandas.core.frame.DataFrame'>
What doesn't really make sense to me, because I can call df3.gender and I get the (entire) expected series. If I remove 'gender' from the line above, it actually group but just that doesn't work for me. Also tried passing the columns names befor groupby, to no avail.
Expected result should be something like this:
sector gender sum
operational male 20
operational female 5
administrative male 10
administrative female 17
sales male 12
sales female 13
IT male 1
IT female 11
Not sure if I can answer to my own question but I think I should since the issue is resolved.
The solutions were very simple, even though I don't understand some of the issues I got.
First problem added on='id' in the merge
df3 = df1[['id','sector']].merge(df2, on='id')
Second problem just missing a list, as pointed by #DYZ
df3.groupby(['sector','gender']).size()
Feeling quite stupid right now... Must be tired. Thanks DYZ and sorry for the trouble.