I am trying to import multiple files in pandas. I have created 3 files in the folder
['File1.xlsx', 'File2.xlsx', 'File3.xlsx'] as read by files = os.listdir(cwd)
import os
import pandas as pd
cwd = os.path.abspath(r'C:\Users\abc\OneDrive\Import Multiple files')
files = os.listdir(cwd)
df = pd.DataFrame()
for file in files:
if file.endswith('.xlsx'):
df = df.append(pd.read_excel(file), ignore_index=True)
df.head()
# df.to_excel('total_sales.xlsx')
print (files)
Upon running the code, I am getting the error (even though the file does exist in the folder)
FileNotFoundError: [Errno 2] No such file or directory: 'File1.xlsx'
Ideally, I want a code where I define a list of files in a LIST and then read the files through the loop using the path and the file LIST.
I think the following should work
import os
import pandas as pd
cwd = os.path.abspath(r'C:\Users\abc\OneDrive\Import Multiple files')
paths = [os.path.join(cwd,path) for path in os.listdir(cwd) if path.endswith('.xlsx')]
df = pd.concat(pd.read_excel(path,ignore_index=True) for path in paths)
df.head()
The idea is to get a list of full paths and then read them all in and concatenate them into a single dataframe on the next line
I'm having issues writing a GeoPandas DataFrame to a shapefile using the GeoDataFrame.to_file() function. When I run the code below, sometimes I get an empty shapefile, and sometimes it runs but returns nothing at all.
import arcpy
import pandas as pd
import glob
import geopandas as gpd
from shapely.geometry import Point
arcpy.env.overwriteOutput = True
arcpy.env.workspace = r'F:\\GY539_Programming\\project_data'
ws = arcpy.env.workspace
files = glob.glob(ws + '/*.csv')
for filename in files:
df = pd.read_csv(filename, sep=',')
geometry = [Point(xy) for xy in zip(df['Longitude'], df['Latitude'])]
gdf = gpd.GeoDataFrame(df, crs='EPSG:4326', geometry=geometry)
gdf.to_file('file.shp', driver='ESRI Shapefile')
Any advice? My data comes from a csv file that contains one column with longitude coordinates and another with latitude coordinates. Here is a snippet of it:
Api Permit ... Latitude Longitude
0 5.000000e+13 163019 ... 61.14 -149.98
1 5.000000e+13 100001 ... 61.21 -149.77
2 5.000000e+13 163015 ... 61.33 -149.91
3 5.000000e+13 165037 ... 61.30 -149.99
4 5.000000e+13 100002 ... 61.42 -149.81
Thanks so much!
Thanks for adding the sample file.
The thing is that if you provide a relative filepath like here
gdf.to_file('file.shp', driver='ESRI Shapefile')
then the file is saved in your current working directory which is maybe not the one where you want the file to appear and therefore do not search there whether the file exists. If you want to save the shapefile to another working directory then just supply an absolute filepath like in this example:
filepath = "C:/users/your/file/path"
gdf.to_file(f"{filepath}/file.shp", driver='ESRI Shapefile')
This worked for me with your sample data. I could also verify this by loading the file again as a GeoDataFrame like this:
gpd_df = gpd.read_file(f"{filepath}/file.shp")
Hope that solves it for you as well.
I have put my CSV file in the same folder as running jupyter notebook, still can't able to import it.
You need read to a df first:
df = pd.read_csv('name.csv') # (the file name of your csv)
df
I want python to take ANY .xls file from given location and save it as .xlsx with original file name? How I can do that so anytime I paste file to location it will be converted to xlsx with original file name?
import pandas as pd
import os
for filename in os.listdir('./'):
if filename.endswith('.xls'):
df = pd.read_excel(filename)
df.to_excel(??)
Your code seems to be perfectly fine. In case you are only missing the correct way to write it with the given name, here you go.
import pandas as pd
import os
for filename in os.listdir('./'):
if filename.endswith('.xls'):
df = pd.read_excel(filename)
df.to_excel(f"{os.path.splitext(filename)[0]}.xlsx")
A possible extension to convert any file that gets pasted inside the folder can be implemented with an infinite loop, for instance:
import pandas as pd
import os
import time
while True:
files = os.listdir('./')
for filename in files:
out_name = f"{os.path.splitext(filename)[0]}.xlsx"
if filename.endswith('.xls') and out_name not in files:
df = pd.read_excel(filename)
df.to_excel(out_name)
time.sleep(10)
I am new to python and I have a scenario where there are multiple parquet files with file names in order. ex: par_file1,par_file2,par_file3 and so on upto 100 files in a folder.
I need to read these parquet files starting from file1 in order and write it to a singe csv file. After writing contents of file1, file2 contents should be appended to same csv without header. Note that all files have same column names and only data is split into multiple files.
I learnt to convert single parquet to csv file using pyarrow with the following code:
import pandas as pd
df = pd.read_parquet('par_file.parquet')
df.to_csv('csv_file.csv')
But I could'nt extend this to loop for multiple parquet files and append to single csv.
Is there a method in pandas to do this? or any other way to do this would be of great help. Thank you.
I ran into this question looking to see if pandas can natively read partitioned parquet datasets. I have to say that the current answer is unnecessarily verbose (making it difficult to parse). I also imagine that it's not particularly efficient to be constantly opening/closing file handles then scanning to the end of them depending on the size.
A better alternative would be to read all the parquet files into a single DataFrame, and write it once:
from pathlib import Path
import pandas as pd
data_dir = Path('dir/to/parquet/files')
full_df = pd.concat(
pd.read_parquet(parquet_file)
for parquet_file in data_dir.glob('*.parquet')
)
full_df.to_csv('csv_file.csv')
Alternatively, if you really want to just append to the file:
data_dir = Path('dir/to/parquet/files')
for i, parquet_path in enumerate(data_dir.glob('*.parquet')):
df = pd.read_parquet(parquet_path)
write_header = i == 0 # write header only on the 0th file
write_mode = 'w' if i == 0 else 'a' # 'write' mode for 0th file, 'append' otherwise
df.to_csv('csv_file.csv', mode=write_mode, header=write_header)
A final alternative for appending each file that opens the target CSV file in "a+" mode at the onset, keeping the file handle scanned to the end of the file for each write/append (I believe this works, but haven't actually tested it):
data_dir = Path('dir/to/parquet/files')
with open('csv_file.csv', "a+") as csv_handle:
for i, parquet_path in enumerate(data_dir.glob('*.parquet')):
df = pd.read_parquet(parquet_path)
write_header = i == 0 # write header only on the 0th file
df.to_csv(csv_handle, header=write_header)
I'm having a similar need and I read current Pandas version supports a directory path as argument for the read_csv function. So you can read multiple parquet files like this:
import pandas as pd
df = pd.read_parquet('path/to/the/parquet/files/directory')
It concats everything into a single dataframe so you can convert it to a csv right after:
df.to_csv('csv_file.csv')
Make sure you have the following dependencies according to the doc:
pyarrow
fastparquet
This helped me to load all parquet files into one data frame
import glob
files = glob.glob("*.snappy.parquet")
data = [pd.read_parquet(f,engine='fastparquet') for f in files]
merged_data = pd.concat(data,ignore_index=True)
If you are going to copy the files over to your local machine and run your code you could do something like this. The code below assumes that you are running your code in the same directory as the parquet files. It also assumes the naming of files as your provided above: "order. ex: par_file1,par_file2,par_file3 and so on upto 100 files in a folder." If you need to search for your files then you will need to get the file names using glob and explicitly provide the path where you want to save the csv: open(r'this\is\your\path\to\csv_file.csv', 'a') Hope this helps.
import pandas as pd
# Create an empty csv file and write the first parquet file with headers
with open('csv_file.csv','w') as csv_file:
print('Reading par_file1.parquet')
df = pd.read_parquet('par_file1.parquet')
df.to_csv(csv_file, index=False)
print('par_file1.parquet appended to csv_file.csv\n')
csv_file.close()
# create your file names and append to an empty list to look for in the current directory
files = []
for i in range(2,101):
files.append(f'par_file{i}.parquet')
# open files and append to csv_file.csv
for f in files:
print(f'Reading {f}')
df = pd.read_parquet(f)
with open('csv_file.csv','a') as file:
df.to_csv(file, header=False, index=False)
print(f'{f} appended to csv_file.csv\n')
You can remove the print statements if you want.
Tested in python 3.6 using pandas 0.23.3
a small change for those trying to read remote files, which helps to read it faster (direct read_parquet for remote files was doing this much slower for me):
import io
merged = []
# remote_reader = ... <- init some remote reader, for example AzureDLFileSystem()
for f in files:
with remote_reader.open(f, 'rb') as f_reader:
merged.append(remote_reader.read())
merged = pd.concat((pd.read_parquet(io.BytesIO(file_bytes)) for file_bytes in merged))
Adds a little temporary memory overhead though.
You can use Dask to read in the multiple Parquet files and write them to a single CSV.
Dask accepts an asterisk (*) as wildcard / glob character to match related filenames.
Make sure to set single_file to True and index to False when writing the CSV file.
import pandas as pd
import numpy as np
# create some dummy dataframes using np.random and write to separate parquet files
rng = np.random.default_rng()
for i in range(3):
df = pd.DataFrame(rng.integers(0, 100, size=(10, 4)), columns=list('ABCD'))
df.to_parquet(f"dummy_df_{i}.parquet")
# load multiple parquet files with Dask
import dask.dataframe as dd
ddf = dd.read_parquet('dummy_df_*.parquet', index=False)
# write to single csv
ddf.to_csv("dummy_df_all.csv",
single_file=True,
index=False
)
# test to verify
df_test = pd.read_csv("dummy_df_all.csv")
Using Dask for this means you won't have to worry about the resulting file size (Dask is a distributed computing framework that can handle anything you throw at it, while pandas might throw a MemoryError if the resulting DataFrame is too large) and you can easily read and write from cloud data storage like Amazon S3.