Learning Python here, and any help on this is much appreciated.
My problem scenario is, there are 2 dataframes A and B contains a column(Name and Flag) list of Names.
ExDF = pd.DataFrame({'Name' : ['Smith','John, Alex','Peter Lin','Carl Marx','Abhraham Moray','Calvin Klein'], 'Flag':['False','False','False','False','False','False']})
SnDF = pd.DataFrame({'Name' : ['Adam K ','John Smith','Peter Lin','Carl Josh','Abhraham Moray','Tim Klein'], 'Flag':['False','False','False','False','False','False']})
The initial value of Flag is False.
Point 1: I need to flip the names in both dataframe ie. Adam Smith to Smith Adam and save the flip names in another new column in the both dataframes.
- This part is done.
Point 2: Then both the Original name and flip names of A dataframe should get check in B dataframe original names and flip names. If it found the the flag column in both the dataframe should get update by True.
I wrote the code but it checks one on one row to both dataframe like A[0] to B[0], A[1] to B[1], but i need to check A[0] record to all the records of B dataframe.
Pls help me on this!!
The code which tried is below:
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
ExDF_swap = ExDF["Swap"] = ExDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
SnDF_swap = SnDF["Swap"] = SnDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
ExDF_swap = pd.DataFrame(ExDF_swap)
SnDF_swap = pd.DataFrame(SnDF_swap)
vect = CountVectorizer()
X = vect.fit_transform(ExDF_swap.Name)
Y = vect.transform(SnDF_swap.Name)
res = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
pd.DataFrame(X.toarray(), columns=vect.get_feature_names())
pd.DataFrame(Y.toarray(), columns=vect.get_feature_names())
ExDF["Flag"] = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
SnDF["Flag"] = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
You could try isin() - of pandas:
import pandas as pd
ExDF = pd.DataFrame({'Name' : ['Smith','John, Alex','Peter Lin','Carl Marx','Abhraham Moray','Calvin Klein'], 'Flag':['False','False','False','False','False','False']})
SnDF = pd.DataFrame({'Name' : ['Adam K ','John Smith','Peter Lin','Carl Josh','Abhraham Moray','Tim Klein'], 'Flag':['False','False','False','False','False','False']})
print(ExDF)
print(SnDF)
ExDF["Swap"] = ExDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
SnDF["Swap"] = SnDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
print(ExDF)
print(SnDF)
ExDF['Flag'] = ExDF.Name.isin(SnDF.Name)
SnDF['Flag'] = SnDF.Name.isin(ExDF.Name)
print(ExDF)
print(SnDF)
Related
I have a dataframe of patients and their gene expressions. I has this format:
Patient_ID | gene1 | gene2 | ... | gene10000
p1 0.142 0.233 ... bla
p2 0.243 0.243 ... -0.364
...
p4000 1.423 bla ... -1.222
As you see, that dataframe contains noise, with cells that are values other then a float value.
I want to remove every row that has a any column with non numeric values.
I've managed to do this using apply and pd.to_numeric like this:
cols = df.columns[1:]
df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
df = df.dropna()
The problem is that it's taking for ever to run, and I need a better and more efficient way of achieving this
EDIT: To reproduce something like my data:
arr = np.random.random_sample((3000,10000))
df = pd.DataFrame(arr, columns=['gene' + str(i) for i in range(10000)])
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(10000)], columns=['Patient_ID']),df],axis = 1)
df['gene0'][2] = 'bla'
df['gene9998'][4] = 'bla'
Was right it is worth trying numpy :)
I got 30-60x times faster version (bigger array, larger improvement)
Convert to numpy array (.values)
Iterate through all rows
Try to convert each row to row of floats
If it fails (some NaN present), note this in boolean array
Create array based on the results
Code:
import pandas as pd
import numpy as np
from line_profiler_pycharm import profile
def op_version(df):
cols = df.columns[1:]
df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
return df.dropna()
def np_version(df):
keep = np.full(len(df), True)
for idx, row in enumerate(df.values[:, 1:]):
try:
row.astype(np.float)
except:
keep[idx] = False
pass # maybe its better to store to_remove list, depends on data
return df[keep]
#profile
def main():
arr = np.random.random_sample((3000, 5000))
df = pd.DataFrame(arr, columns=['gene' + str(i) for i in range(5000)])
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(3000)],
columns=['Patient_ID']), df], axis=1)
df['gene0'][2] = 'bla'
df['gene998'][4] = 'bla'
df2 = df.copy()
df = op_version(df)
df2 = np_version(df2)
Note I decreased number of columns so it is more feasible for tests.
Also, fixed small bug in your example, instead of:
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(10000)], columns=['Patient_ID']),df],axis = 1)
I think should be
df = pd.concat([pd.DataFrame(['p' + str(i) for i in range(3000)], columns=['Patient_ID']),df],axis = 1)
Say I have a pd.DataFrame() that I differenced with .diff(5), which works like "new number at idx i = (number at idx i) - (number at idx i-5)"
import pandas as pd
import random
example_df = pd.DataFrame(data=random.sample(range(1, 100), 20), columns=["number"])
df_diff = example_df.diff(5)
Now I want to undo this operation using the first 5 entries of example_df, and using df_diff.
If i had done .diff(1), I would simply use .cumsum(). But how can I achieve that it only sums up every 5th value?
My desired output is a df with the following values:
df_example[0]
df_example[1]
df_example[2]
df_example[3]
df_example[4]
df_diff[5] + df_example[0]
df_diff[6] + df_example[1]
df_diff[7] + df_example[2]
df_diff[8] + df_example[3]
...
you could shift the column, add them and fill nans:
df_diff["shifted"] = example_df.shift(5)
df_diff["undone"] = df_diff["number"] + df_diff["shifted"]
df_diff["undone"] = df_diff["undone"].fillna(example_df["number"])
Following my previous question, now i'm trying to put data in a table and convert it to an excel file but i can't get the table i want, if anyone can help or explain what's the cause of it, this is the final output i want to get
this the data i'm printing
Hotel1 : chambre double - {'lpd': ('112', '90','10'), 'pc': ('200', '140','10')}
and here is my code
import pandas as pd
import ast
s="Hotel1 : chambre double - {'lpd': ('112', '90','10'), 'pc': ('200', '140','10')}"
ds = []
for l in s.splitlines():
d = l.split("-")
if len(d) > 1:
df = pd.DataFrame(ast.literal_eval(d[1].strip()))
ds.append(df)
for df in ds:
df.reset_index(drop=True, inplace=True)
df = pd.concat(ds, axis= 1)
cols = df.columns
cols = [((col.split('.')[0], col)) for col in df.columns]
df.columns=pd.MultiIndex.from_tuples(cols)
print(df.T)
df.to_excel("v.xlsx")
but this is what i get
How can i solve the probleme please this the final and most important part and thank you in advance.
Within the for loop, the value "Hotel1 : chambre double" is held in d[0]
(try it by yourself by printing d[0].)
In your previous question, the "Name3" column was built by the following line of code:
cols = [((col.split('.')[0], col)) for col in df.columns]
Now, to save "Hotel1 : chambre double", you need to access it within the first for loop.
import pandas as pd
import ast
s="Hotel1 : chambre double - {'lpd': ('112', '90','10'), 'pc': ('200', '140','10')}"
ds = []
cols = []
for l in s.splitlines():
d = l.split("-")
if len(d) > 1:
df = pd.DataFrame(ast.literal_eval(d[1].strip()))
ds.append(df)
cols2 = df.columns
cols = [((d[0], col)) for col in df.columns]
for df in ds:
df.reset_index(drop=True, inplace=True)
df = pd.concat(ds, axis= 1)
df.columns=pd.MultiIndex.from_tuples(cols)
print(df.T)
df.T.to_csv(r"v.csv")
This works, because you are taking the d[0] (hotel name) within the for loop, and creating tuples for your column names whilst you have access to that object.
you then create a multi index column in the line of code you already had, outside the loop:
df.columns=pd.MultiIndex.from_tuples(cols)
Finally, to answer the output to excel query you had, please add the following line of code at the bottom:
df.T.to_csv(r"v.csv")
EDITED
I want to write an If loop with conditions on cooncatenating strings.
i.e. If cell A1 contains a specific format of text, then only do you concatenate, else leave as is.
example:
If bill number looks like: CM2/0000/, then concatenate this string with the date column (month - year), else leave the bill number as it is.
Sample Data
You can create function which does what you need and use df.apply() to execute it on all rows.
I use example data from #Boomer answer.
EDIT: you didn't show what you really have in dataframe and it seems you have datetime in bill_date but I used strings. I had to convert strings to datetime to show how to work with this. And now it needs .strftime('%m-%y') or sometimes .dt.strftime('%m-%y') instead of .str[3:].str.replace('/','-'). Because pandas uses different formats to display dateitm for different countries so I couldn't use str(x) for this because it gives me 2019-09-15 00:00:00 instead of yours 15/09/19
import pandas as pd
df = pd.DataFrame({
'bill_number': ['CM2/0000/', 'CM2/0000', 'CM3/0000/', 'CM3/0000'],
'bill_date': ['15/09/19', '15/09/19', '15/09/19', '15/09/19']
})
df['bill_date'] = pd.to_datetime(df['bill_date'])
def convert(row):
if row['bill_number'].endswith('/'):
#return row['bill_number'] + row['bill_date'].str[3:].replace('/','-')
return row['bill_number'] + row['bill_date'].strftime('%m-%y')
else:
return row['bill_number']
df['bill_number'] = df.apply(convert, axis=1)
print(df)
Result:
bill_number bill_date
0 CM2/0000/09-19 15/09/19
1 CM2/0000 15/09/19
2 CM3/0000/09-19 15/09/19
3 CM3/0000 15/09/19
Second idea is to create mask
mask = df['bill_number'].str.endswith('/')
and later use it for all values
#df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].str[3:].str.replace('/','-')
df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].dt.strftime('%m-%y')
or
#df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].str[3:].str.replace('/','-')
df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].dt.strftime('%m-%y')
Left side needs .loc[mask,'bill_number'] instead of `[mask]['bill_number'] to correctly assing values - but right side doesn't need it.
import pandas as pd
df = pd.DataFrame({
'bill_number': ['CM2/0000/', 'CM2/0000', 'CM3/0000/', 'CM3/0000'],
'bill_date': ['15/09/19', '15/09/19', '15/09/19', '15/09/19']
})
df['bill_date'] = pd.to_datetime(df['bill_date'])
mask = df['bill_number'].str.endswith('/')
#df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].str[3:].str.replace('/','-')
# or
#df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].str[3:].str.replace('/','-')
df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].dt.strftime('%m-%y')
#or
#df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].dt.strftime('%m-%y')
print(df)
Third idea is to use numpy.where()
import pandas as pd
import numpy as np
df = pd.DataFrame({
'bill_number': ['CM2/0000/', 'CM2/0000', 'CM3/0000/', 'CM3/0000'],
'bill_date': ['15/09/19', '15/09/19', '15/09/19', '15/09/19']
})
df['bill_date'] = pd.to_datetime(df['bill_date'])
df['bill_number'] = np.where(
df['bill_number'].str.endswith('/'),
#df['bill_number'] + df['bill_date'].str[3:].str.replace('/','-'),
df['bill_number'] + df['bill_date'].dt.strftime('%m-%y'),
df['bill_number'])
print(df)
Maybe this will work for you. It would be nice to have a data sample like #Mike67 was stating. But based on your information this is what I came up with. Bulky, but it works. I'm sure someone else will have a fancier version.
import pandas as pd
from pandas import DataFrame, Series
dat = {'num': ['CM2/0000/','CM2/0000', 'CM3/0000/', 'CM3/0000',],
'date': ['15/09/19','15/09/19','15/09/19','15/09/19']}
df = pd.DataFrame(dat)
df['date'] = df['date'].map(lambda x: str(x)[3:])
df['date'] = df['date'].str.replace('/','-')
for cols in df.columns:
df.loc[df['num'].str.endswith('/'), cols] = df['num'] + df['date']
print(df)
Results:
num date
0 CM2/0000/09-19 09-19
1 CM2/0000 09-19
2 CM3/0000/09-19 09-19
3 CM3/0000 09-19
I have two dataframes, and trying to find out a way to match the exact substring from one dataframe to another dataframe.
First DataFrame:
import pandas as pd
import numpy as np
random_data = {'Place Name':['TS~HOT_MD~h_PB~progra_VV~gogl', 'FM~uiosv_PB~emo_SZ~1x1_TG~bhv'],
'Site':['DV360', 'Adikteev']}
dataframe = pd.DataFrame(random_data)
print(dataframe)
Second DataFrame
test_data = {'code name': ['PB', 'PB', 'PB'],
'Actual':['programmatic me', 'emoteev', 'programmatic-mechanics'],
'code':['progra', 'emo', 'prog']}
test_dataframe = pd.DataFrame(test_data)
Approach
for k, l, m in zip(test_dataframe.iloc[:, 0], test_dataframe.iloc[:, 1], test_dataframe.iloc[:, 2]):
dataframe['Site'] = np.select([dataframe['Place Name'].str.contains(r'\b{}~{}\b'.format(k, m), regex=False)], [l],
default=dataframe['Site'])
The current output is as below, though I am expecting to match the exact substring, which is not working with the code above.
Current Output:
Place Name Site
TS~HOT_MD~h_PB~progra_VV~gogl programmatic-mechanics
FM~uiosv_PB~emo_SZ~1x1_TG~bhv emoteev
Expected Output:
Place Name Site
TS~HOT_MD~h_PB~progra_VV~gogl programmatic me
FM~uiosv_PB~emo_SZ~1x1_TG~bhv emoteev
Data
import pandas as pd
import numpy as np
random_data = {'Place Name':['TS~HOT_MD~h_PB~progra_VV~gogl',
'FM~uiosv_PB~emo_SZ~1x1_TG~bhv'], 'Site':['DV360', 'Adikteev']}
dataframe = pd.DataFrame(random_data)
test_data = {'code name': ['PB', 'PB', 'PB'], 'Actual':['programmatic me', 'emoteev', 'programmatic-mechanics'],
'code':['progra', 'emo', 'prog']}
test_dataframe = pd.DataFrame(test_data)
Map the test_datframe code and Actual into dictionary as key and value respectively
keys=test_dataframe['code'].values.tolist()
dicto=dict(zip(test_dataframe.code, test_dataframe.Actual))
dicto
Join the keys separated by | to enable search of either phrases
k = '|'.join(r"{}".format(x) for x in dicto.keys())
k
Extract string from datframe meeting any of the phrases in k and map them to to the dictionary
dataframe['Site'] = dataframe['Place Name'].str.extract('('+ k + ')', expand=False).map(dicto)
dataframe
Output
Not the most elegant solution, but this does the trick.
Set up data
import pandas as pd
import numpy as np
random_data = {'Place Name':['TS~HOT_MD~h_PB~progra_VV~gogl',
'FM~uiosv_PB~emo_SZ~1x1_TG~bhv'], 'Site':['DV360', 'Adikteev']}
dataframe = pd.DataFrame(random_data)
test_data = {'code name': ['PB', 'PB', 'PB'], 'Actual':['programmatic me', 'emoteev', 'programmatic-mechanics'],
'code':['progra', 'emo', 'prog']}
test_dataframe = pd.DataFrame(test_data)
Solution
Create a column in test_dataframe with the substring to match:
test_dataframe['match_str'] = test_dataframe['code name'] + '~' + test_dataframe.code
print(test_dataframe)
code name Actual code match_str
0 PB programmatic me progra PB~progra
1 PB emoteev emo PB~emo
2 PB programmatic-mechanics prog PB~prog
Define a function to apply to test_dataframe:
def match_string(row, dataframe):
ind = row.name
try:
if row[-1] in dataframe.loc[ind, 'Place Name']:
return row[1]
else:
return dataframe.loc[ind, 'Site']
except KeyError:
# More rows in test_dataframe than there are in dataframe
pass
# Apply match_string and assign back to dataframe
dataframe['Site'] = test_dataframe.apply(match_string, args=(dataframe,), axis=1)
Output:
Place Name Site
0 TS~HOT_MD~h_PB~progra_VV~gogl programmatic me
1 FM~uiosv_PB~emo_SZ~1x1_TG~bhv emoteev