I would like to read several CSV files from a directory into pandas and concatenate them into one big DataFrame. I have not been able to figure it out though. Here is what I have so far:
import glob
import pandas as pd
# Get data file names
path = r'C:\DRO\DCL_rawdata_files'
filenames = glob.glob(path + "/*.csv")
dfs = []
for filename in filenames:
dfs.append(pd.read_csv(filename))
# Concatenate all data into one DataFrame
big_frame = pd.concat(dfs, ignore_index=True)
I guess I need some help within the for loop?
See pandas: IO tools for all of the available .read_ methods.
Try the following code if all of the CSV files have the same columns.
I have added header=0, so that after reading the CSV file's first row, it can be assigned as the column names.
import pandas as pd
import glob
import os
path = r'C:\DRO\DCL_rawdata_files' # use your path
all_files = glob.glob(os.path.join(path , "/*.csv"))
li = []
for filename in all_files:
df = pd.read_csv(filename, index_col=None, header=0)
li.append(df)
frame = pd.concat(li, axis=0, ignore_index=True)
Or, with attribution to a comment from Sid.
all_files = glob.glob(os.path.join(path, "*.csv"))
df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)
It's often necessary to identify each sample of data, which can be accomplished by adding a new column to the dataframe.
pathlib from the standard library will be used for this example. It treats paths as objects with methods, instead of strings to be sliced.
Imports and Setup
from pathlib import Path
import pandas as pd
import numpy as np
path = r'C:\DRO\DCL_rawdata_files' # or unix / linux / mac path
# Get the files from the path provided in the OP
files = Path(path).glob('*.csv') # .rglob to get subdirectories
Option 1:
Add a new column with the file name
dfs = list()
for f in files:
data = pd.read_csv(f)
# .stem is method for pathlib objects to get the filename w/o the extension
data['file'] = f.stem
dfs.append(data)
df = pd.concat(dfs, ignore_index=True)
Option 2:
Add a new column with a generic name using enumerate
dfs = list()
for i, f in enumerate(files):
data = pd.read_csv(f)
data['file'] = f'File {i}'
dfs.append(data)
df = pd.concat(dfs, ignore_index=True)
Option 3:
Create the dataframes with a list comprehension, and then use np.repeat to add a new column.
[f'S{i}' for i in range(len(dfs))] creates a list of strings to name each dataframe.
[len(df) for df in dfs] creates a list of lengths
Attribution for this option goes to this plotting answer.
# Read the files into dataframes
dfs = [pd.read_csv(f) for f in files]
# Combine the list of dataframes
df = pd.concat(dfs, ignore_index=True)
# Add a new column
df['Source'] = np.repeat([f'S{i}' for i in range(len(dfs))], [len(df) for df in dfs])
Option 4:
One liners using .assign to create the new column, with attribution to a comment from C8H10N4O2
df = pd.concat((pd.read_csv(f).assign(filename=f.stem) for f in files), ignore_index=True)
or
df = pd.concat((pd.read_csv(f).assign(Source=f'S{i}') for i, f in enumerate(files)), ignore_index=True)
An alternative to darindaCoder's answer:
path = r'C:\DRO\DCL_rawdata_files' # use your path
all_files = glob.glob(os.path.join(path, "*.csv")) # advisable to use os.path.join as this makes concatenation OS independent
df_from_each_file = (pd.read_csv(f) for f in all_files)
concatenated_df = pd.concat(df_from_each_file, ignore_index=True)
# doesn't create a list, nor does it append to one
import glob
import os
import pandas as pd
df = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', "my_files*.csv"))))
Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional third-party libraries. You can do this in two lines using everything Pandas and Python (all versions) already have built in.
For a few files - one-liner
df = pd.concat(map(pd.read_csv, ['d1.csv', 'd2.csv','d3.csv']))
For many files
import os
filepaths = [f for f in os.listdir(".") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))
For No Headers
If you have specific things you want to change with pd.read_csv (i.e., no headers) you can make a separate function and call that with your map:
def f(i):
return pd.read_csv(i, header=None)
df = pd.concat(map(f, filepaths))
This pandas line, which sets the df, utilizes three things:
Python's map (function, iterable) sends to the function (the
pd.read_csv()) the iterable (our list) which is every CSV element
in filepaths).
Panda's read_csv() function reads in each CSV file as normal.
Panda's concat() brings all these under one df variable.
Easy and Fast
Import two or more CSV files without having to make a list of names.
import glob
import pandas as pd
df = pd.concat(map(pd.read_csv, glob.glob('data/*.csv')))
The Dask library can read a dataframe from multiple files:
>>> import dask.dataframe as dd
>>> df = dd.read_csv('data*.csv')
(Source: https://examples.dask.org/dataframes/01-data-access.html#Read-CSV-files)
The Dask dataframes implement a subset of the Pandas dataframe API. If all the data fits into memory, you can call df.compute() to convert the dataframe into a Pandas dataframe.
I googled my way into Gaurav Singh's answer.
However, as of late, I am finding it faster to do any manipulation using NumPy and then assigning it once to a dataframe rather than manipulating the dataframe itself on an iterative basis and it seems to work in this solution too.
I do sincerely want anyone hitting this page to consider this approach, but I don't want to attach this huge piece of code as a comment and making it less readable.
You can leverage NumPy to really speed up the dataframe concatenation.
import os
import glob
import pandas as pd
import numpy as np
path = "my_dir_full_path"
allFiles = glob.glob(os.path.join(path,"*.csv"))
np_array_list = []
for file_ in allFiles:
df = pd.read_csv(file_,index_col=None, header=0)
np_array_list.append(df.as_matrix())
comb_np_array = np.vstack(np_array_list)
big_frame = pd.DataFrame(comb_np_array)
big_frame.columns = ["col1", "col2"....]
Timing statistics:
total files :192
avg lines per file :8492
--approach 1 without NumPy -- 8.248656988143921 seconds ---
total records old :1630571
--approach 2 with NumPy -- 2.289292573928833 seconds ---
A one-liner using map, but if you'd like to specify additional arguments, you could do:
import pandas as pd
import glob
import functools
df = pd.concat(map(functools.partial(pd.read_csv, sep='|', compression=None),
glob.glob("data/*.csv")))
Note: map by itself does not let you supply additional arguments.
If you want to search recursively (Python 3.5 or above), you can do the following:
from glob import iglob
import pandas as pd
path = r'C:\user\your\path\**\*.csv'
all_rec = iglob(path, recursive=True)
dataframes = (pd.read_csv(f) for f in all_rec)
big_dataframe = pd.concat(dataframes, ignore_index=True)
Note that the three last lines can be expressed in one single line:
df = pd.concat((pd.read_csv(f) for f in iglob(path, recursive=True)), ignore_index=True)
You can find the documentation of ** here. Also, I used iglobinstead of glob, as it returns an iterator instead of a list.
EDIT: Multiplatform recursive function:
You can wrap the above into a multiplatform function (Linux, Windows, Mac), so you can do:
df = read_df_rec('C:\user\your\path', *.csv)
Here is the function:
from glob import iglob
from os.path import join
import pandas as pd
def read_df_rec(path, fn_regex=r'*.csv'):
return pd.concat((pd.read_csv(f) for f in iglob(
join(path, '**', fn_regex), recursive=True)), ignore_index=True)
Inspired from MrFun's answer:
import glob
import pandas as pd
list_of_csv_files = glob.glob(directory_path + '/*.csv')
list_of_csv_files.sort()
df = pd.concat(map(pd.read_csv, list_of_csv_files), ignore_index=True)
Notes:
By default, the list of files generated through glob.glob is not sorted. On the other hand, in many scenarios, it's required to be sorted e.g. one may want to analyze number of sensor-frame-drops v/s timestamp.
In pd.concat command, if ignore_index=True is not specified then it reserves the original indices from each dataframes (i.e. each individual CSV file in the list) and the main dataframe looks like
timestamp id valid_frame
0
1
2
.
.
.
0
1
2
.
.
.
With ignore_index=True, it looks like:
timestamp id valid_frame
0
1
2
.
.
.
108
109
.
.
.
IMO, this is helpful when one may want to manually create a histogram of number of frame drops v/s one minutes (or any other duration) bins and want to base the calculation on very first timestamp e.g.
begin_timestamp = df['timestamp'][0]
Without, ignore_index=True, df['timestamp'][0] generates the series containing very first timestamp from all the individual dataframes, it does not give just a value.
Another one-liner with list comprehension which allows to use arguments with read_csv.
df = pd.concat([pd.read_csv(f'dir/{f}') for f in os.listdir('dir') if f.endswith('.csv')])
Alternative using the pathlib library (often preferred over os.path).
This method avoids iterative use of pandas concat()/apped().
From the pandas documentation:
It is worth noting that concat() (and therefore append()) makes a full copy of the data, and that constantly reusing this function can create a significant performance hit. If you need to use the operation over several datasets, use a list comprehension.
import pandas as pd
from pathlib import Path
dir = Path("../relevant_directory")
df = (pd.read_csv(f) for f in dir.glob("*.csv"))
df = pd.concat(df)
If multiple CSV files are zipped, you may use zipfile to read all and concatenate as below:
import zipfile
import pandas as pd
ziptrain = zipfile.ZipFile('yourpath/yourfile.zip')
train = []
train = [ pd.read_csv(ziptrain.open(f)) for f in ziptrain.namelist() ]
df = pd.concat(train)
Based on Sid's good answer.
To identify issues of missing or unaligned columns
Before concatenating, you can load CSV files into an intermediate dictionary which gives access to each data set based on the file name (in the form dict_of_df['filename.csv']). Such a dictionary can help you identify issues with heterogeneous data formats, when column names are not aligned for example.
Import modules and locate file paths:
import os
import glob
import pandas
from collections import OrderedDict
path =r'C:\DRO\DCL_rawdata_files'
filenames = glob.glob(path + "/*.csv")
Note: OrderedDict is not necessary, but it'll keep the order of files which might be useful for analysis.
Load CSV files into a dictionary. Then concatenate:
dict_of_df = OrderedDict((f, pandas.read_csv(f)) for f in filenames)
pandas.concat(dict_of_df, sort=True)
Keys are file names f and values are the data frame content of CSV files.
Instead of using f as a dictionary key, you can also use os.path.basename(f) or other os.path methods to reduce the size of the key in the dictionary to only the smaller part that is relevant.
import os
os.system("awk '(NR == 1) || (FNR > 1)' file*.csv > merged.csv")
Where NR and FNR represent the number of the line being processed.
FNR is the current line within each file.
NR == 1 includes the first line of the first file (the header), while FNR > 1 skips the first line of each subsequent file.
In case of an unnamed column issue, use this code for merging multiple CSV files along the x-axis.
import glob
import os
import pandas as pd
merged_df = pd.concat([pd.read_csv(csv_file, index_col=0, header=0) for csv_file in glob.glob(
os.path.join("data/", "*.csv"))], axis=0, ignore_index=True)
merged_df.to_csv("merged.csv")
You can do it this way also:
import pandas as pd
import os
new_df = pd.DataFrame()
for r, d, f in os.walk(csv_folder_path):
for file in f:
complete_file_path = csv_folder_path+file
read_file = pd.read_csv(complete_file_path)
new_df = new_df.append(read_file, ignore_index=True)
new_df.shape
Consider using convtools library, which provides lots of data processing primitives and generates simple ad hoc code under the hood.
It is not supposed to be faster than pandas/polars, but sometimes it can be.
e.g. you could concat csv files into one for further reuse - here's the code:
import glob
from convtools import conversion as c
from convtools.contrib.tables import Table
import pandas as pd
def test_pandas():
df = pd.concat(
(
pd.read_csv(filename, index_col=None, header=0)
for filename in glob.glob("tmp/*.csv")
),
axis=0,
ignore_index=True,
)
df.to_csv("out.csv", index=False)
# took 20.9 s
def test_convtools():
table = None
for filename in glob.glob("tmp/*.csv"):
table_ = Table.from_csv(filename, header=False)
if table is None:
table = table_
else:
table = table.chain(table_)
table.into_csv("out_convtools.csv", include_header=False)
# took 15.8 s
Of course if you just want to obtain a dataframe without writing a concatenated file, it will take 4.63 s and 10.9 s correspondingly (pandas is faster here because it doesn't need to zip columns for writing it back).
import pandas as pd
import glob
path = r'C:\DRO\DCL_rawdata_files' # use your path
file_path_list = glob.glob(path + "/*.csv")
file_iter = iter(file_path_list)
list_df_csv = []
list_df_csv.append(pd.read_csv(next(file_iter)))
for file in file_iter:
lsit_df_csv.append(pd.read_csv(file, header=0))
df = pd.concat(lsit_df_csv, ignore_index=True)
This is how you can do it using Colaboratory on Google Drive:
import pandas as pd
import glob
path = r'/content/drive/My Drive/data/actual/comments_only' # Use your path
all_files = glob.glob(path + "/*.csv")
li = []
for filename in all_files:
df = pd.read_csv(filename, index_col=None, header=0)
li.append(df)
frame = pd.concat(li, axis=0, ignore_index=True,sort=True)
frame.to_csv('/content/drive/onefile.csv')
Much has been written about the downsides of using inplace=True when working on data frames, but am I mistaken to assume that renaming columns with inplace=True is benign?
Do any data blocks get copied and discarded when I do
df.rename(columns={'old': 'new'}, inplace=True)
Simple timing tests show that renaming columns in place is faster than assigning a copy:
df = df.rename(columns={'old': 'new'})
Much faster actually for large data frames. The time renaming in place takes does not depend on the size of the dataframe.
import numpy as np
import pandas as pd
import time
# np.random.seed(0)
df = pd.DataFrame(np.random.rand(10**6,5), columns=list('abcde'))
d1 = dict(zip(list('abcde'),list('ABCDE')))
d2 = dict(zip(list('ABCDE'),list('abcde')))
t0 = time.perf_counter()
for i in range(10):
df.rename(columns=d1, inplace=True)
df.rename(columns=d2, inplace=True)
t1 = time.perf_counter()
for i in range(10):
df = df.rename(columns=d1)
df = df.rename(columns=d2)
t2 = time.perf_counter()
print('inplace : ', t1-t0)
print('df = df : ', t2-t1)
I am using Python 3.9.6 and Pandas 1.3.1. under Win10 and get:
inplace : 0.003490000000000215
df = df : 0.1703701999999998
Can I conclude that no copies are made behind the scenes?
The time renaming in place takes does not depend on the size of the dataframe. Can I conclude that no copies are made behind the scenes?
Yes, you can conclude that, except that a copy of the column names series may be made. Obviously the performance of that should be immaterial as the number of columns is usually not huge.
I'm SUPER green to Python and am having some issues trying to automate some calculations.
I know that this works to add a new column called "Returns" that divides "value" of current to "value" of previous to a csv:
import pandas as pd
import numpy as np
import csv
a = pd.read_csv("/Data/a_data.csv", index_col = "time")
a ["Returns"] = (a["value"]/a["value"].shift(1) -1)*100
However, I have a lot of these CSVs. I need this calculation to happen prior to merging them all together. So I was hoping to write something that just looped through all of the CSVs and did the calculation and added the column but clearly this was incorrect as I get Syntax error:
import pandas as pd
import numpy as np
import csv
a = pd.read_csv("/Data/a_data.csv", index_col = "time")
b = pd.read_csv("/Data/b_data.csv", index_col = "time")
c = pd.read_csv("/Data/c_data.csv", index_col = "time")
my_lists = ['a','b','c']
for my_list in my_lists:
{my_list}["Returns"] = ({my_list}["close"]/{my_list}["close"].shift(1) -1)*100
print(f"Calculating: {my_list.upper()}")
I'm sure there is an easy way to do this that I just haven't reached in my Python education yet, so any guidance would be greatly appreciated!
Assuming "close" and "time" are fields defined in each of your csv files, you could define a function that reads each file, do the shift and returns a dataframe:
def your_func(my_file): # this function takes a file name as an argument.
my_df = pd.read_csv(my_file, index_col = "time") # The function reads its content into a data frame,
my_df["Returns"] = (my_df["close"]/{my_df}["close"].shift(1) -1)*100 # makes the calculation
return my_df #and returns it as an output.
Then as a main code, you collect all csv files from a folder with glob package. Using the above function, you build a data frame for each file with the calculation done.
import glob
path =r'/Data/' # path to the directory where you have the csv files
filenames = glob.glob(path + "/*.csv") # grab the csv files names using glob package with path+all csv files present
for filename in filenames: # loop into all csv files names in the list of csv files present in the directory
df= your_func (filename) # call the function, defined above block of code, that reads the file from its name as argument, then makes the calculation and returns it.
print (df)
Above, there is a print of the data Frame which shows results; I am not sure what you intend to do with upper (I dont think this is a function on a data frame).
Finally, this returns independent data frames with calculations done prior to other or final transformation.
1.Do a, b, c data frames have the same dimension?
2.You don't need to import the CSV library because it includes in the Pandas library.
3.If you want to union data frames, you can use like this :
my_lists = [a,b,c]
and you can concatenate with this way:
result=pd.concat(my_lists)
Lastly, your calculation should be :
result["Returns"]=(result.loc[:, "close"].div(result.loc[:, "close"].shift()).fillna(0).replace([np.inf, -np.inf], 0))
You need to add an index-label selection (loc) function to the data frame in order to access the values. When numbers are dividing, results can be NaN(Not a Number) or infinite. Therefore, replace and fillna functions are related to NaN and Inf.
I am importing 4000+ csv files all with the same columns, columns=['Date', 'Datapint'] the importing the csv's to dask is pretty straight forward and is working fine for me.
file_paths = '/root/data/daily/'
df = dd.read_csv(file_paths+'*.csv',
delim_whitespace=True,
names=['Date','Datapoint'])
The task I am trying to achive is to be able to name the 'Datapoint' column the filename of the .csv. I know you can set a column to the path using include_path_column = True. But I am wondering if there is a simple way use that pathname as a column name with out having to run a separate step down the line.
I was able to do this (fairly straight forward) using dask's delayed function:
import pandas as pd
import dask.dataframe as dd
from dask import delayed
import glob
path = r'/root/data/daily' # use your path
file_list = glob.glob(path + "/*.csv")
def read_and_label_csv(filename):
# reads each csv file to a pandas.DataFrame
df_csv = pd.read_csv(filename,
delim_whitespace=True,
names=['Date','Close'])
df_csv.rename(columns={'Close':path_2_column}, inplace=True)
return df_csv
# create a list of functions ready to return a pandas.DataFrame
dfs = [delayed(read_and_label_csv)(fname) for fname in file_list]
# using delayed, assemble the pandas.DataFrames into a dask.DataFrame
ddf = dd.from_delayed(dfs)
It is unclear to me what exactly you are trying to accomplish. If you are just trying to change the name of the column that the filepaths are written to, you can set include_path_column='New Column Name'. If you are naming a column based on the path to each file, it seems like you'll get a rather sparse array once the data are concatenated and I would argue that a groupby would probably work better.
I have a .csv file locally. I am reading the file with pandas. I want to move the df.info() result into an excel. Looks like df.info().to_excel does not work as it is not supported. Is there any way to do this?
I tried df.info().to_excel
import pandas as pd
from openpyxl.workbook import Workbook
pd.read_csv("file.csv",sep='|', error_bad_lines=False)
writer = pd.ExcelWriter('output.xlsx')
df.info()
df.info().to_excel(writer,sheet_name='info')
I want to show the dataframe info output in a single tab of the excel.
The easiest way for me is to get the same information in dataframes, but separately:
df_datatypes = pd.DataFrame(df.dtypes)
df_null_count = df.count()
Then write to excel as usual.
to_excel is a method of the DataFrame https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html and DataFrame.info() doesn't return a DataFrame
You can write the info to a text file like so:
import io
buffer = io.StringIO()
df.info(buf=buffer)
s = buffer.getvalue()
with open("df_info.txt", "w", encoding="utf-8") as f:
f.write(s)
You can modify this code by removing last two lines and parsing the s variable and creating a DataFrame out of it (in the way you would like this to appear in the excel file) and then use the to_excel() method.
I agree with #yl_low but you could have a more elegant solution as shown:
def get_dataframe_info(df):
"""
input
df -> DataFrame
output
df_null_counts -> DataFrame Info (sorted)
"""
df_types = pd.DataFrame(df.dtypes)
df_nulls = df.count()
df_null_count = pd.concat([df_types, df_nulls], axis=1)
df_null_count = df_null_count.reset_index()
# Reassign column names
col_names = ["features", "types", "non_null_counts"]
df_null_count.columns = col_names
# Add this to sort
df_null_count = df_null_count.sort_values(by=["null_counts"], ascending=False)
return df_null_count
You can do this in Python 3.
pd.DataFrame({"name": train.columns, "non-nulls": len(train)-train.isnull().sum().values, "nulls": train.isnull().sum().values, "type": train.dtypes.values}).to_excel("op.xlsx")
Just one line code (without non-null column);
df.dtypes.reset_index(name='Dtype').rename(columns={'index' : 'Column'}).to_excel(pd.ExcelWriter('Name.xlsx'), 'info')