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Remove non numeric rows from dataframe

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)

Combining Successive Pandas Dataframes in One Master Dataframe via a Loop

I'm trying to loop through a series of tickers cleaning the associated dataframes then combining the individual ticker dataframes into one large dataframe with columns named for each ticker. The following code enables me to loop through unique tickers and name the columns of each ticker's dataframe after the specific ticker:
import pandas as pd
def clean_func(tkr,f1):
f1['Date'] = pd.to_datetime(f1['Date'])
f1.index = f1['Date']
keep = ['Col1','Col2']
f2 = f1[keep]
f2.columns = [tkr+'Col1',tkr+'Col2']
return f2
tkrs = ['tkr1','tkr2','tkr3']
for tkr in tkrs:
df1 = pd.read_csv(f'C:\\path\\{tkr}.csv')
df2 = clean_func(tkr,df1)
However, I don't know how to create a master dataframe where I add each new ticker to the master dataframe. With that in mind, I'd like to align each new ticker's data using the datetime index. So, if tkr1 has data for 6/25/22, 6/26/22, 6/27/22, and tkr2 has data for 6/26/22, and 6/27/22, the combined dataframe would show all three dates but would produce a NaN for ticker 2 on 6/25/22 since there is no data for that ticker on that date.
When not in a loop looking to append each successive ticker to a larger dataframe (as per above), the following code does what I'd like. But it doesn't work when looping and adding new ticker data for each successive loop (or I don't know how to make it work in the confines of a loop).
combined = pd.concat((df1, df2, df3,...,dfn), axis=1)
Many thanks in advance.

You should only create the master DataFrame after the loop. Appending to the master DataFrame in each iteration via pandas.concat is slow since you are creating a new DataFrame every time.
Instead, read each ticker DataFrame, clean it, and append it to a list which store every ticker DataFrames. After the loop create the master DataFrame with all the Dataframes using pandas.concat:
import pandas as pd
def clean_func(tkr,f1):
f1['Date'] = pd.to_datetime(f1['Date'])
f1.index = f1['Date']
keep = ['Col1','Col2']
f2 = f1[keep]
f2.columns = [tkr+'Col1',tkr+'Col2']
return f2
tkrs = ['tkr1','tkr2','tkr3']
dfs_list = []
for tkr in tkrs:
df1 = pd.read_csv(f'C:\\path\\{tkr}.csv')
df2 = clean_func(tkr,df1)
dfs_list.append(df2)
master_df = pd.concat(dfs_list, axis=1)
As a suggestion here is a cleaner way of defining your clean_func using DataFrame.set_index and DataFrame.add_prefix.
def clean_func(tkr, f1):
f1['Date'] = pd.to_datetime(f1['Date'])
f2 = f1.set_index('Date')[['Col1','Col2']].add_prefix(tkr)
return f2
Or if you want, you can parse the Date column as datetime and set it as index directly in the pd.read_csv call by specifying index_col and parse_dates parameters (honestly, I'm not sure if those two parameters will play well together, and I'm too lazy to test it, but you can try ;)).
import pandas as pd
def clean_func(tkr,f1):
f2 = f1[['Col1','Col2']].add_prefix(tkr)
return f2
tkrs = ['tkr1','tkr2','tkr3']
dfs_list = []
for tkr in tkrs:
df1 = pd.read_csv(f'C:\\path\\{tkr}.csv', index_col='Date', parse_dates=['Date'])
df2 = clean_func(tkr,df1)
dfs_list.append(df2)
master_df = pd.concat(dfs_list, axis=1)

Before the loop create an empty df with:
combined = pd.DataFrame()
Then within the loop (after loading df1 - see code above):
combined = pd.concat((combined, clean_func(tkr, df1)), axis=1)
If you get:
TypeError: concat() got multiple values for argument 'axis'
Make sure your parentheses are correct per above.
With the code above, you can skip the original step:
df2 = clean_func(tkr,df1)
Since it is embedded in the concat function. Alternatively, you could keep the df2 step and use:
combined = pd.concat((combined,df2), axis=1)
Just make sure the dataframes are encapsulated by parentheses within the concat function.

Same answer as GC123 but here is a full example which mimics reading from separate files and concatenating them
import pandas as pd
import io
fake_file_1 = io.StringIO("""
fruit,store,quantity,unit_price
apple,fancy-grocers,2,9.25
pear,fancy-grocers,3,100
banana,fancy-grocers,1,256
""")
fake_file_2 = io.StringIO("""
fruit,store,quantity,unit_price
banana,bargain-grocers,667,0.01
apple,bargain-grocers,170,0.15
pear,bargain-grocers,281,0.45
""")
fake_files = [fake_file_1,fake_file_2]
combined = pd.DataFrame()
for fake_file in fake_files:
df = pd.read_csv(fake_file)
df = df.set_index('fruit')
combined = pd.concat((combined, df), axis=1)
print(combined)
Output

This method is slightly more efficient:
combined = []
for fake_file in fake_files:
combined.append(pd.read_csv(fake_file).set_index('fruit'))
combined = pd.concat(combined, axis=1)
print(combined)
Output:
store quantity unit_price store quantity unit_price
fruit
apple fancy-grocers 2 9.25 bargain-grocers 170 0.15
pear fancy-grocers 3 100.00 bargain-grocers 281 0.45
banana fancy-grocers 1 256.00 bargain-grocers 667 0.01

divide the row into two rows after several columns

I have CSV file and I try to split my row into many rows if it contains more than 4 columns
Example:-
enter image description here
Expected Output:
enter image description here
So there are way to do that in pandas or python
Sorry if this is a simple question

When there are two columns with the same name in CSV file, the pandas dataframe automatically appends an integer value to the duplicate column name
for example:
This CSV file :
Will become this :
df = pd.read_csv("Book1.csv")
df
Now to solve your question, lets consider the above dataframe as the input dataframe.
Try this :
cols = df.columns.tolist()
cols.remove('id')
start = 0
end = 4
new_df = []
final_cols = ['id','x1','y1','x2','y2']
while start<len(cols):
if end>len(cols):
end = len(cols)
temp = cols[start:end]
start = end
end = end+4
temp_df = df.loc[:,['id']+temp]
temp_df.columns = final_cols[:1+len(temp)]
if len(temp)<4:
temp_df[final_cols[1+len(temp):]] = None
print(temp_df)
new_df.append(temp_df)
pd.concat(new_df).reset_index(drop = True)
Result:

You can first set the video column as index then concat your remaining every 4 columns into a new dataframe. At last, reset index to get video column back.
df.set_index('video', inplace=True)
dfs = []
for i in range(len(df.columns)//4):
d = df.iloc[:, range(i*4,i*4+4)]
dfs.append(d.set_axis(['x_center', 'y_center']*2, axis=1))
df_ = pd.concat(dfs).reset_index()
I think the following list comprehension should work, but it gives an positional indexing error on my machine and I don't know why
df_ = pd.concat([df.iloc[: range(i*4, i*4+4)].set_axis(['x_center', 'y_center']*2, axis=1) for i in range(len(df.columns)//4)])
print(df_)
video x_center y_center x_center y_center
0 1_1 31.510973 22.610222 31.383655 22.488293
1 1_1 31.856295 22.830109 32.016905 22.948702
2 1_1 32.011684 22.990689 31.933356 23.004779

Loop through cell range (Every 3 cells) and add ranking to it

The problem is I am trying to make a ranking for every 3 cells in that column
using pandas.
For example:
This is the outcome I want
I have no idea how to make it.
I tried something like this:
for i in range(df.iloc[1:],df.iloc[,:],3):
counter = 0
i['item'] += counter + 1
The code is completely wrong, but I need help with the range and put df.iloc in the brackets in pandas.

Does this match the requirements ?
import pandas as pd
df = pd.DataFrame()
df['Item'] = ['shoes','shoes','shoes','shirts','shirts','shirts']
df2 = pd.DataFrame()
for i, item in enumerate(df['Item'].unique(), 1):
df2.loc[i-1,'rank'] = i
df2.loc[i-1, 'Item'] = item
df2['rank'] = df2['rank'].astype('int')
print(df)
print("\n")
print(df2)
df = df.merge(df2, on='Item', how='inner')
print("\n")
print(df)

Pandas create new column every time function runs

This is data.csv:
tickers = ['ACOR', 'ACM', 'ACLS', 'ACND', 'ACMR']
stats = ['mkt_cap', 'price', 'change']
This code creates a csv file for each stat in the assets directory:
date = str(dt.date.today())
for stat in stats:
df = pd.read_csv('data.csv')
df.set_index('ticker', inplace=True)
df = df.loc[tickers, ['{}'.format(stat)]]
date = str(dt.date.today())
df.rename(columns = {'{}'.format(stat):date}, inplace=True)
df.to_csv(assets/{}.csv'.format(stats))
Here is price.csv
ticker 2019/07/04
ACOR 7.42
ACM 37.33
... ...
The problem is I need a new column to be created every time this function is run with the current date as the header. Data.csv gets updated everyday and I would like to add new data into mkt_cap.csv, prices.csv and change.csv with the new date as the header. The updated prices.csv would look like:
ticker 2019/07/04 2019/07/05
ACOR 7.42 XXX
ACM 37.33 XXX
... ...
EDIT:
date = str(dt.date.today())
for stat in stats:
df = pd.read_csv('data.csv')
df.set_index('ticker', inplace=True)
df = df.loc[tickers, ['{}'.format(stat)]]
date = str(dt.date.today())
df.rename(columns = {'{}'.format(stat):date}, inplace=True)
df.to_csv(assets/{}.csv'.format(stats))
for col in stats.columns:
stats["{}-{}".format(dt.date.today(),col)] = stats[col]
dataframes = []
for datapoint in stats.columns[-5:-1]:
dataframes.append(stats[[datapoint, "ticker"]])
for dff in dataframes:
dff.to_csv('assets/{}.csv'.format(dff.columns[1]))

import pandas as pd
list1 = []
for i in range(0,10):
list1.append(i)
df = pd.DataFrame()
df["col1"] = list1
df['col2'] = df['col1']+5
import datetime as dt
def new_col(df):
df[dt.datetime.now()] = df['col1']+ df['col2']
return df
new_col(df)
This will create a new column when the function is called with the datetime the function is run. Not entirely sure what you are trying to do as far as the arithmetic of the new column but this should do the trick as far as creating the new column.

for col in acor.columns: #or you could just use your stat list
acor["{}-{}".format(dt.datetime.now(),col)] = acor[col]
dataframes = [] ##seperates into individual dataframes
for datapoint in acor.columns[-5:-1]:
dataframes.append(acor[[datapoint,"timestamp"]])###you probobly want to replace timestamp with "symbol" or "ticker"
###finally saves dataframes by date and stat
for dff in dataframes:
dff.to_csv("{}.csv".format(dff.columns[1]))