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I can't higlight cell in Excell from Pandas Dataframe

I have an issue with my code to highlight specific cell in an excel file when I export my DF. Cells to highlight with background colors are outliers of the column. Oultiers are calculated thanks a for loop on each column.
Here the code where I calculate outliers for each column:
for col in dfmg.columns.difference(['Sbj', 'expertise', 'gender']):
Q1c = dfmg[col].quantile(0.25)
Q3c = dfmg[col].quantile(0.75)
IQRc = Q3 - Q1
lowc = Q1-1.5*IQR
uppc = Q3+1.5*IQR
Then I created this function to define how to higlight cells:
def colors(v):
for v in dfmg[col]:
if v < lowc or v > uppc:
color = 'yellow'
return 'background-color: %s' % color
And I apply my function to a new df:
df_colored = dfmg.style.applymap(colors)
The problem is that when I export df_colored, everything is yellow! Where am i wrong?
Thanks for help!

You can create DataFrame of styles and apply to DataFrame, I modify your solution for no loops:
def hightlight(x):
c1 = 'background-color: yellow'
cols = x.columns.difference(['Sbj', 'expertise', 'gender'])
Q1 = x[cols].quantile(0.25)
Q3 = x[cols].quantile(0.75)
IQR = Q3 - Q1
low = Q1-1.5*IQR
up = Q3+1.5*IQR
mask = x.lt(low) | x.gt(up)
#DataFrame with same index and columns names as original filled empty strings
df1 = pd.DataFrame(c2, index=x.index, columns=x.columns)
#modify values of df1 column by boolean mask
df1[cols] = df1[cols].mask(mask, c1)
return df1
dfng.style.apply(hightlight, axis=None)

Check if column values exists in different dataframe

I have a pandas DataFrame 'df' with x rows, and another pandas DataFrame 'df2' with y rows
(x < y). I want to return the indexes of where the values of df['Farm'] equals the value of df2['Fields'], in order to add respective 'Manager' to df.
the code I have is as follows:
data2 = [['field1', 'Paul G'] , ['field2', 'Mark R'], ['field3', 'Roy Jr']]
data = [['field1'] , ['field2']]
columns = ['Field']
columns2 = ['Field', 'Manager']
df = pd.DataFrame(data, columns=columns)
df2 = pd.DataFrame(data2, columns=columns2)
farmNames = df['Farm']
exists = farmNames.reset_index(drop=True) == df1['Field'].reset_index(drop=True)
This returns the error message:
ValueError: Can only compare identically-labeled Series objects
Does anyone know how to fix this?

As #NickODell mentioned, you could use a merge, basically a left join. See below code.
df_new = pd.merge(df, df2, on = 'Field', how = 'left')
print(df_new)
Output:
Field Manager
0 field1 Paul G
1 field2 Mark R

Color dataframe rows by condition in Pandas

I need to color rows in my DataFrame, according to the condition depending on some column. For example:
test = pd.DataFrame({"A": [1,2,3,4,5], "B":[5,3,2,1,4]})
def color(score):
return f"background-color:" + (" #ffff00;" if score < 4 else "#ff0000")
test.style.applymap(color, subset = ["A"])
But in this way I get color only at A column:
Also i can code like this:
def color1(score):
return f"background-color: #ffff00;"
def color2(score):
return f"background-color: #ff0000;"
temp = test.style.applymap(color1, subset = (test[test["A"]< 4].index, slice(None)))
temp.applymap(color2, subset = (test[test["A"] >= 4].index, slice(None)))
In this way color will be ok, but i struggle multiple calls of applymap functions. Is there any way to fulfill my goal without the multiple calls?

In the first example, by the subset='A' you are telling to apply only to column A. If you remove that it will apply to the entire dataframe.
import pandas as pd
test = pd.DataFrame({"A": [1,2,3,4,5], "B":[5,3,2,1,4]})
def color(score):
return f"background-color:" + (" #ffff00;" if score < 4 else "#ff0000")
test.style.applymap(color)
If you want to apply different formatting in different columns you can easily design something handling one column at a time.
def color2(score):
return f"background-color: #80FFff;" if score < 4 else None
test.style.applymap(color, subset='A') \
.applymap(color2, subset='B')

If some complicated mask or set styles is possible create DataFrame of styles and pass to Styler.apply with axis=None:
def highlight(x):
c1 = f"background-color: #ffff00;"
c2 = f"background-color: #ff0000;"
m = x["A"]< 4
# DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
# set columns by condition
df1.loc[m, :] = c1
df1.loc[~m, :] = c2
#for check DataFrame of styles
print (df1)
return df1
test.style.apply(highlight, axis=None)

Highlight panda df errors based on conditions

Good day SO community,
I have been having an issue with trying to highlight errors in my df, row by row.
reference_dict = {'jobclass' : ['A','B'], 'Jobs' : ['Teacher','Plumber']}
dict = {'jobclass': ['A','C','A'], 'Jobs': ['Teacher', 'Plumber','Policeman']}
df = pd.DataFrame(data=dict)
def highlight_rows(df):
for i in df.index:
if df.jobclass[i] in reference_dict['jobclass']:
print(df.jobclass[i])
return 'background-color: green'
df.style.apply(highlight_rows, axis = 1)
I am getting the error:
TypeError: ('string indices must be integers', 'occurred at index 0')
What i hope to get is my df with values not found in my reference_dict being highlighted.
Any help would be greatly appreciated.. Cheers!
Edit:
x = {'jobclass' : ['A','B'], 'Jobs' : ['Teacher','Plumber']}
d = {'jobclass': ['A','C','A'], 'Jobs': ['Teacher', 'Plumber','Policeman']}
df = pd.DataFrame(data=d)
print(df)
def highlight_rows(s):
ret = ["" for i in s.index]
for i in df.index:
if df.jobclass[i] not in x['jobclass']:
ret[s.index.get_loc('Jobs')] = "background-color: yellow"
return ret
df.style.apply(highlight_rows, axis = 1)
Tried this and got the whole column highlighted instead of the specific rows values that i desire.. =/

You can use merge with parameter indicator for found not matched values and then create DataFrame of styles:
x = {'jobclass' : ['A','B'], 'Jobs' : ['Teacher','Plumber']}
d = {'jobclass': ['A','C','A'], 'Jobs': ['Teacher', 'Plumber','Policeman']}
df = pd.DataFrame(data=d)
print (df)
jobclass Jobs
0 A Teacher
1 C Plumber
2 A Policeman
Detail:
print (df.merge(pd.DataFrame(x) , on='jobclass', how='left', indicator=True))
jobclass Jobs_x Jobs_y _merge
0 A Teacher Teacher both
1 C Plumber NaN left_only
2 A Policeman Teacher both
def highlight_rows(s):
c1 = 'background-color: yellow'
c2 = ''
df1 = pd.DataFrame(x)
m = s.merge(df1, on='jobclass', how='left', indicator=True)['_merge'] == 'left_only'
df2 = pd.DataFrame(c2, index=s.index, columns=s.columns)
df2.loc[m, 'Jobs'] = c1
return df2
df.style.apply(highlight_rows, axis = None)

Good day to you as well!
What i hope to get is my df with values not found in my reference_dict being highlighted.
If you're looking for values not found in reference_dict to be highlighted, do you mean for the function to be the following?
def highlight_rows(df):
for i in df.index:
if df.jobclass[i] not in reference_dict['jobclass']:
print(df.jobclass[i])
return 'background-color: green'
Either way, why highlight the rows when you could isolate them? It seems like you want to look at all of the job classes in df where there is not one in reference_dict.
import pandas as pd
reference_dict = {'jobclass' : ['A','B'], 'Jobs' : ['Teacher','Plumber']}
data_dict = {'jobclass': ['A','C','A'], 'Jobs': ['Teacher', 'Plumber','Policeman']}
ref_df = pd.DataFrame(reference_dict)
df = pd.DataFrame(data_dict)
outliers = df.merge(ref_df, how='outer', on='jobclass') # merge the two tables together, how='outer' includes jobclasses which the DataFrames do not have in common. Will automatically generate columns Jobs_x and Jobs_y once joined together because the columns have the same name
outliers = outliers[ outliers['Jobs_y'].isnull() ] # Jobs_y is null when there is no matching jobclass in the reference DataFrame, so we can take advantage of that by filtering
outliers = outliers.drop('Jobs_y', axis=1) # let's drop the junk column after we used it to filter for what we wanted
print("The reference DataFrame is:")
print(ref_df,'\n')
print("The input DataFrame is:")
print(df,'\n')
print("The result is a list of all the jobclasses not in the reference DataFrame and what job is with it:")
print(outliers)
The result is:
The reference DataFrame is:
jobclass Jobs
0 A Teacher
1 B Plumber
The input DataFrame is:
jobclass Jobs
0 A Teacher
1 C Plumber
2 A Policeman
The result is a list of all the jobclasses not in the reference DataFrame and what job is with it:
jobclass Jobs_x
2 C Plumber
This could have been a tangent but it's what I'd do. I was not aware you could highlight rows in pandas at all, cool trick.

Diff between two dataframes in pandas

I have two dataframes both of which have the same basic schema. (4 date fields, a couple of string fields, and 4-5 float fields). Call them df1 and df2.
What I want to do is basically get a "diff" of the two - where I get back all rows that are not shared between the two dataframes (not in the set intersection). Note, the two dataframes need not be the same length.
I tried using pandas.merge(how='outer') but I was not sure what column to pass in as the 'key' as there really isn't one and the various combinations I tried were not working. It is possible that df1 or df2 has two (or more) rows that are identical.
What is a good way to do this in pandas/Python?

Try this:
diff_df = pd.merge(df1, df2, how='outer', indicator='Exist')
diff_df = diff_df.loc[diff_df['Exist'] != 'both']
You will have a dataframe of all rows that don't exist on both df1 and df2.

IIUC:
You can use pd.Index.symmetric_difference
pd.concat([df1, df2]).loc[
df1.index.symmetric_difference(df2.index)
]

You can use this function, the output is an ordered dict of 6 dataframes which you can write to excel for further analysis.
'df1' and 'df2' refers to your input dataframes.
'uid' refers to the column or combination of columns that make up the unique key. (i.e. 'Fruits')
'dedupe' (default=True) drops duplicates in df1 and df2. (refer to Step 4 in comments)
'labels' (default = ('df1','df2')) allows you to name the input dataframes. If a unique key exists in both dataframes, but have
different values in one or more columns, it is usually important to know these rows, put them one on top of the other and label the row with the name so we know to which dataframe does it belong to.
'drop' can take a list of columns to be excluded from the consideration when considering the difference
Here goes:
df1 = pd.DataFrame([['apple', '1'], ['banana', 2], ['coconut',3]], columns=['Fruits','Quantity'])
df2 = pd.DataFrame([['apple', '1'], ['banana', 3], ['durian',4]], columns=['Fruits','Quantity'])
dict1 = diff_func(df1, df2, 'Fruits')
In [10]: dict1['df1_only']:
Out[10]:
Fruits Quantity
1 coconut 3
In [11]: dict1['df2_only']:
Out[11]:
Fruits Quantity
3 durian 4
In [12]: dict1['Diff']:
Out[12]:
Fruits Quantity df1 or df2
0 banana 2 df1
1 banana 3 df2
In [13]: dict1['Merge']:
Out[13]:
Fruits Quantity
0 apple 1
Here is the code:
import pandas as pd
from collections import OrderedDict as od
def diff_func(df1, df2, uid, dedupe=True, labels=('df1', 'df2'), drop=[]):
dict_df = {labels[0]: df1, labels[1]: df2}
col1 = df1.columns.values.tolist()
col2 = df2.columns.values.tolist()
# There could be columns known to be different, hence allow user to pass this as a list to be dropped.
if drop:
print ('Ignoring columns {} in comparison.'.format(', '.join(drop)))
col1 = list(filter(lambda x: x not in drop, col1))
col2 = list(filter(lambda x: x not in drop, col2))
df1 = df1[col1]
df2 = df2[col2]
# Step 1 - Check if no. of columns are the same:
len_lr = len(col1), len(col2)
assert len_lr[0]==len_lr[1], \
'Cannot compare frames with different number of columns: {}.'.format(len_lr)
# Step 2a - Check if the set of column headers are the same
# (order doesnt matter)
assert set(col1)==set(col2), \
'Left column headers are different from right column headers.' \
+'\n Left orphans: {}'.format(list(set(col1)-set(col2))) \
+'\n Right orphans: {}'.format(list(set(col2)-set(col1)))
# Step 2b - Check if the column headers are in the same order
if col1 != col2:
print ('[Note] Reordering right Dataframe...')
df2 = df2[col1]
# Step 3 - Check datatype are the same [Order is important]
if set((df1.dtypes == df2.dtypes).tolist()) - {True}:
print ('dtypes are not the same.')
df_dtypes = pd.DataFrame({labels[0]:df1.dtypes,labels[1]:df2.dtypes,'Diff':(df1.dtypes == df2.dtypes)})
df_dtypes = df_dtypes[df_dtypes['Diff']==False][[labels[0],labels[1],'Diff']]
print (df_dtypes)
else:
print ('DataType check: Passed')
# Step 4 - Check for duplicate rows
if dedupe:
for key, df in dict_df.items():
if df.shape[0] != df.drop_duplicates().shape[0]:
print(key + ': Duplicates exists, they will be dropped.')
dict_df[key] = df.drop_duplicates()
# Step 5 - Check for duplicate uids.
if type(uid)==str or type(uid)==list:
print ('Uniqueness check: {}'.format(uid))
for key, df in dict_df.items():
count_uid = df.shape[0]
count_uid_unique = df[uid].drop_duplicates().shape[0]
var = [0,1][count_uid_unique == df.shape[0]] #<-- Round off to the nearest integer if it is 100%
pct = round(100*count_uid_unique/df.shape[0], var)
print ('{}: {} out of {} are unique ({}%).'.format(key, count_uid_unique, count_uid, pct))
# Checks complete, begin merge. '''Remenber to dedupe, provide labels for common_no_match'''
dict_result = od()
df_merge = pd.merge(df1, df2, on=col1, how='inner')
if not df_merge.shape[0]:
print ('Error: Merged DataFrame is empty.')
else:
dict_result[labels[0]] = df1
dict_result[labels[1]] = df2
dict_result['Merge'] = df_merge
if type(uid)==str:
uid = [uid]
if type(uid)==list:
df1_only = df1.append(df_merge).reset_index(drop=True)
df1_only['Duplicated']=df1_only.duplicated(keep=False) #keep=False, marks all duplicates as True
df1_only = df1_only[df1_only['Duplicated']==False]
df2_only = df2.append(df_merge).reset_index(drop=True)
df2_only['Duplicated']=df2_only.duplicated(keep=False)
df2_only = df2_only[df2_only['Duplicated']==False]
label = labels[0]+' or '+labels[1]
df_lc = df1_only.copy()
df_lc[label] = labels[0]
df_rc = df2_only.copy()
df_rc[label] = labels[1]
df_c = df_lc.append(df_rc).reset_index(drop=True)
df_c['Duplicated'] = df_c.duplicated(subset=uid, keep=False)
df_c1 = df_c[df_c['Duplicated']==True]
df_c1 = df_c1.drop('Duplicated', axis=1)
df_uc = df_c[df_c['Duplicated']==False]
df_uc_left = df_uc[df_uc[label]==labels[0]]
df_uc_right = df_uc[df_uc[label]==labels[1]]
dict_result[labels[0]+'_only'] = df_uc_left.drop(['Duplicated', label], axis=1)
dict_result[labels[1]+'_only'] = df_uc_right.drop(['Duplicated', label], axis=1)
dict_result['Diff'] = df_c1.sort_values(uid).reset_index(drop=True)
return dict_result

Set df2.columns = df1.columns
Now, set every column as the index: df1 = df1.set_index(df1.columns.tolist()), and similarly for df2.
You can now do df1.index.difference(df2.index), and df2.index.difference(df1.index), and the two results are your distinct columns.

with
left_df.merge(df,left_on=left_df.columns.tolist(),right_on=df.columns.tolist(),how='outer')
you can get the outer join result.
Similarly, you can get the inner join result.Then make a diff that would be what you want.