I'm working with several csv with Pandas. I changed some data name on the original csv file and saved the file. Then, I restarted and reloaded my jupyter notebook but now I got something like this for all dataframe I charged the data source :
Department Zone Element Product Year Unit Value
0 U1,"Z3","ODD 2.a.1... NaN NaN NaN NaN NaN NaN
1 U1,"Z3","ODD 2.a.1... NaN NaN NaN NaN NaN NaN
2 U1,"Z5","ODD 2.a.1... NaN NaN NaN NaN NaN NaN
3 U1,"Z6","ODD 2.a.1... NaN NaN NaN NaN NaN NaN
4 U1,"Z9","ODD 2.a.1... NaN NaN NaN NaN NaN NaN
I tried to use sep=',', encoding='UTF-8-SIG',quotechar='"', quoting=0, engine='python' but same issue. I don't know how to parse the csv because even when I created a new csv form the data (without the quote and separator as ; ) the same issue appears...
csv is 321 rows, as this example with the problem : https://www.cjoint.com/c/LDCmfvq06R6
and original csv file without problem in Pandas : https://www.cjoint.com/c/LDCmlweuR66
I thing problem with quotes of the file
import csv
df = pd.read_csv('LDCmfvq06R6_FAOSTAT.csv', quotechar='"',
delimiter = ',',
quoting=csv.QUOTE_NONE,
on_bad_lines='skip')
for i, col in enumerate(df.columns):
df.iloc[:, i] = df.iloc[:, i].str.replace('"', '')
df.head()
Related
Good afternoon
I am trying to import more than a 100 separate .txt files containing data I want to plot. I would like to automise this process, since doing the same iteration for every individual file is most tedious.
I have read up on how to read multiple .txt files, and found a nice explanation. However, following the example all my data gets imported as NaNs. I read up some more and found a more reliable way of importing .txt files, namely by using pd.read_fwf() as can be seen here.
Although I can at least see my data now, I have no clue how to plot it, since the data is in one column separated by \t, e.g.
0 Extension (mm)\tLoad (kN)\tMachine extension (mm)\tPreload extension
1 0.000000\t\t\t
2 0.152645\t0.000059312\t.....
... etc.
I have tried using different separators in both the pd.read_csv() and pd.read_fwf() including ' ', '\t' and '-s+', but to now avail.
Of course this causes a problem, because now I can not plot my data. Speaking of, I am also not sure how to plot the data in the dataframe. I want to plot each .txt file's data separately on the same scatter plot.
I am very new to stack overflow, so pardon the format of the question if it does not conform to the normal standard. I attach my code below, but unfortunately I can not attach my .txt files. Each .txt file contains about a thousand rows of data. I attach a picture of the general format of all the files. General format of the .txt files.
import numpy as np
import pandas as pd
from matplotlib import pyplot as pp
import os
import glob
# change the working directory
os.chdir(r"C:\Users\Philip de Bruin\Desktop\Universiteit van Pretoria\Nagraads\sterktetoetse_basislyn\trektoetse\speel")
# get the file names
leggername = [i for i in glob.glob("*.txt")]
# put everything in a dataframe
df = [pd.read_fwf(legger) for legger in leggername]
df
EDIT: the output I get now for the DataFrame is:
[ Time (s)\tLoad (kN)\tMachine Extension (mm)\tExtension
0
1 0.000000\t\t\t
2
3 0.152645\t0.000059312\t-...
4
... ...
997 76.0173\t0.037706\t0.005...
998
999 76.1699\t0.037709\t\t
1000
1001
from Preload (mm)
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
... ... ... ...
997 NaN NaN NaN
998 NaN NaN NaN
999 NaN NaN NaN
1000 NaN NaN NaN
1001 NaN NaN NaN
[1002 rows x 4 columns],
Time (s)\tLoad (kN)\tMachine Extension (mm)\tExtension
0
1 0.000000\t\t\t
2
3 0.128151\t0.000043125\t-...
4
... ...
997 63.8191\t0.034977\t-0.00...
998
999 63.9473\t0.034974\t\t
1000
1001
from Preload (mm)
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
... ... ... ...
997 NaN NaN NaN
998 NaN NaN NaN
999 NaN NaN NaN
1000 NaN NaN NaN
1001 NaN NaN NaN
[1002 rows x 4 columns],
Time (s)\tLoad (kN)\tMachine Extension (mm)\tExtension
0
1 0.000000\t\t\t
2
3 0.174403\t0.000061553\t0...
4
... ...
997 86.8529\t0.036093\t-0.00...
998
999 87.0273\t\t-0.0059160\t-...
1000
1001
from Preload (mm)
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
... ... ... ...
997 NaN NaN NaN
998 NaN NaN NaN
999 NaN NaN NaN
1000 NaN NaN NaN
1001 NaN NaN NaN
... etc
The basic gist is to skip the first data row (that has a single value in it), then read the individual files with pd.read_csv, using tab as the separator, and stack them together.
There is, however, a more problematic issue: the data files turn out to be UTF-16 encoded (the binary data show a NUL character at the even positions), but there is no byte-order-mark (BOM) to indicate this. As a result, you can't specify the encoding in read_csv, but have to manually read each file as binary, then decode it with UTF-16 to a string, then feed that string to read_csv. Since the latter requires a filename or IO-stream, the text data needs to be put into a StringIO object first (or save the corrected data to disk first, then read the corrected file; might not be a bad idea).
import pandas as pd
import os
import glob
import io
# change the working directory
os.chdir(r"C:\Users\Philip de Bruin\Desktop\Universiteit van Pretoria\Nagraads\sterktetoetse_basislyn\trektoetse\speel")
dfs = []
for filename in glob.glob("*.txt"):
with open(filename, 'rb') as fp:
data = fp.read() # a single file should fit in memory just fine
# Decode the UTF-16 data that is missing a BOM
string = data.decode('UTF-16')
# And put it into a stream, for ease-of-use with `read_csv`
stream = io.StringIO(string)
# Read the data from the, now properly decoded, stream
# Skip the single-value row, and use tabs as separators
df = pd.read_csv(stream, sep='\t', skiprows=[1])
# To keep track of the individual files, add an "origin" column
# with its value set to the corresponding filename
df['origin'] = filename
dfs.append(df)
# Concate all dataframes (default is to stack the rows)
df = pd.concat(dfs)
# For a quick and dirty plot, you can enjoy the power of Seaborn
import seaborn as sns
# Use appropriate (full) column names, and use the 'origin'
# column for the hue and symbol
sns.scatterplot(data=df, x='Time (s)', y='Machine Extension (mm)', hue='origin', style='origin')
Seaborn's scatterplot documentation.
I manually create a DataFrame:
import pandas as pd
df_articles1 = pd.DataFrame({'Id' : [4,5,8,9],
'Class':[
{'encourage': 1, 'contacting': 1},
{'cardinality': 16, 'subClassOf': 3},
{'get-13.5.1': 1},
{'cardinality': 12, 'encourage': 1}
]
})
I export it to a csv file to import after splitting it:
df_articles1.to_csv(f"""{path}articles_split.csv""", index = False, sep=";")
I can split it with pd.json_normalize():
df_articles1 = pd.json_normalize(df_articles1['Class'])
I import its csv file to a DataFrame:
df_articles2 = pd.read_csv(f"""{path}articles_split.csv""", sep=";")
But this fails with:
AttributeError: 'str' object has no attribute 'values' pd.json_normalize(df_articles2['Class'])
that was because when you save by to_csv() the data in your 'Class' column is stored as string not as dictionary/json so after loading that saved data:
df_articles2 = pd.read_csv(f"""{path}articles_split.csv""", sep=";")
Then to make it back in original form make use of eval() method and apply() method:-
df_articles2['Class']=df_articles2['Class'].apply(lambda x:eval(x))
Finally:
resultdf=pd.json_normalize(df_articles2['Class'])
Now If you print resultdf you will get your desired output
While the accepted answer works, using eval is bad practice.
To parse a string column that looks like JSON/dict, use one of the following options (last one is best, if possible).
ast.literal_eval (better)
import ast
objects = df2['Class'].apply(ast.literal_eval)
normed = pd.json_normalize(objects)
df2[['Id']].join(normed)
# Id encourage contacting cardinality subClassOf get-13.5.1
# 0 4 1.0 1.0 NaN NaN NaN
# 1 5 NaN NaN 16.0 3.0 NaN
# 2 8 NaN NaN NaN NaN 1.0
# 3 9 1.0 NaN 12.0 NaN NaN
json.loads (even better)
import json
objects = df2['Class'].apply(json.loads)
normed = pd.json_normalize(objects)
df2[['Id']].join(normed)
# encourage contacting cardinality subClassOf get-13.5.1
# 0 1.0 1.0 NaN NaN NaN
# 1 NaN NaN 16.0 3.0 NaN
# 2 NaN NaN NaN NaN 1.0
# 3 1.0 NaN 12.0 NaN NaN
If the strings are single quoted, use str.replace to convert them to double quotes (and thus valid JSON) before applying json.loads:
objects = df2['Class'].str.replace("'", '"').apply(json.loads)
normed = pd.json_normalize(objects)
df2[['Id']].join(normed)
pd.json_normalize before pd.to_csv (recommended)
If possible, when you originally save to CSV, just save the normalized JSON (not raw JSON objects):
df1 = df1[['Id']].join(pd.json_normalize(df1['Class']))
df1.to_csv('df1_normalized.csv', index=False, sep=';')
# Id;encourage;contacting;cardinality;subClassOf;get-13.5.1
# 4;1.0;1.0;;;
# 5;;;16.0;3.0;
# 8;;;;;1.0
# 9;1.0;;12.0;;
This is a more natural CSV workflow (rather than storing/loading object blobs):
df2 = pd.read_csv('df1_normalized.csv', sep=';')
# Id encourage contacting cardinality subClassOf get-13.5.1
# 0 4 1.0 1.0 NaN NaN NaN
# 1 5 NaN NaN 16.0 3.0 NaN
# 2 8 NaN NaN NaN NaN 1.0
# 3 9 1.0 NaN 12.0 NaN NaN
I am trying to read a deliminated text file into a dataframe in python. The deliminator is not being identified when I use pd.read_table. If I explicitly set sep = ' ', I get an error: Error tokenizing data. C error. Notably the defaults work when I use np.loadtxt().
Example:
pd.read_table('http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt',
comment = '%',
header = None)
0
0 1850 1 -0.777 0.412 NaN NaN...
1 1850 2 -0.239 0.458 NaN NaN...
2 1850 3 -0.426 0.447 NaN NaN...
3 1850 4 -0.680 0.367 NaN NaN...
4 1850 5 -0.687 0.298 NaN NaN...
If I set sep = ' ', I get another error:
pd.read_table('http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt',
comment = '%',
header = None,
sep = ' ')
ParserError: Error tokenizing data. C error: Expected 2 fields in line 78, saw 58
Looking up this error, people suggest using header = None (already done) and setting sep = explicitly, but that is causing the problem: Python Pandas Error tokenizing data. I looked up line 78 and can't see any problems. If I set error_bad_lines=False i get an empty df suggesting there is a problem with every entry.
Notably this works when I use np.loadtxt():
pd.DataFrame(np.loadtxt('http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt',
comments = '%'))
0 1 2 3 4 5 6 7 8 9 10 11
0 1850.0 1.0 -0.777 0.412 NaN NaN NaN NaN NaN NaN NaN NaN
1 1850.0 2.0 -0.239 0.458 NaN NaN NaN NaN NaN NaN NaN NaN
2 1850.0 3.0 -0.426 0.447 NaN NaN NaN NaN NaN NaN NaN NaN
3 1850.0 4.0 -0.680 0.367 NaN NaN NaN NaN NaN NaN NaN NaN
4 1850.0 5.0 -0.687 0.298 NaN NaN NaN NaN NaN NaN NaN NaN
This suggests to me that there isn't something wrong with the file, but rather with how I am calling pd.read_table(). I looked through the documentation for np.loadtxt() in the hope of setting the sep to the same value, but that just shows: delimiter=None (https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html).
I'd prefer to be able to import this as a pd.DataFrame, setting the names, rather than having to import as a matrix and then convert to pd.DataFrame.
What am I getting wrong?
This one is quite tricky. Please try out the snippet code below:
import pandas as pd
url = 'http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt'
df = pd.read_csv(url,
sep='\s+',
comment='%',
usecols=(0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11),
names=('Year', 'Month', 'M.Anomaly', 'M.Unc.', 'A.Anomaly',
'A.Unc.','5y.Anomaly', '5y.Unc.' ,'10y.Anomaly', '10y.Unc.',
'20y.Anomaly', '20y.Unc.'))
The issue is the file has 77 rows of commented text, for 'Global Average Temperature Anomaly with Sea Ice Temperature Inferred from Air Temperatures'
Two of the rows are headers
There's a bunch of data, then there are two more headers, and a new set of data for 'Global Average Temperature Anomaly with Sea Ice Temperature Inferred from Water Temperatures'
This solution separates the two tables in the file into separate dataframes.
This is not as nice as the other answer, but the data is properly separated into different dataframes.
The headers were a pain, it would probably be easier to manually create a custom header, and skip the lines of code for separating the headers from the text.
The important point separating air and ice data.
import requests
import pandas as pd
import math
# read the file with requests
url = 'http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt'
response = requests.get(url)
data = response.text
# convert data into a list
data = [d.strip().replace('% ', '') for d in data.split('\n')]
# specify the data from the ranges in the file
air_header1 = data[74].split() # not used
air_header2 = [v.strip() for v in data[75].split(',')]
# combine the 2 parts of the header into a single header
air_header = air_header2[:2] + [f'{air_header1[math.floor(i/2)]}_{v}' for i, v in enumerate(air_header2[2:])]
air_data = [v.split() for v in data[77:2125]]
h2o_header1 = data[2129].split() # not used
h2o_header2 = [v.strip() for v in data[2130].split(',')]
# combine the 2 parts of the header into a single header
h2o_header = h2o_header2[:2] + [f'{h2o_header1[math.floor(i/2)]}_{v}' for i, v in enumerate(h2o_header2[2:])]
h2o_data = [v.split() for v in data[2132:4180]]
# create the dataframes
air = pd.DataFrame(air_data, columns=air_header)
h2o = pd.DataFrame(h2o_data, columns=h2o_header)
Without the header code
Simplify the code, by using a manual header list.
import pandas as pd
import requests
# read the file with requests
url = 'http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt'
response = requests.get(url)
data = response.text
# convert data into a list
data = [d.strip().replace('% ', '') for d in data.split('\n')]
# manually created header
headers = ['Year', 'Month', 'Monthly_Anomaly', 'Monthly_Unc.',
'Annual_Anomaly', 'Annual_Unc.',
'Five-year_Anomaly', 'Five-year_Unc.',
'Ten-year_Anomaly', 'Ten-year_Unc.',
'Twenty-year_Anomaly', 'Twenty-year_Unc.']
# separate the air and h2o data
air_data = [v.split() for v in data[77:2125]]
h2o_data = [v.split() for v in data[2132:4180]]
# create the dataframes
air = pd.DataFrame(air_data, columns=headers)
h2o = pd.DataFrame(h2o_data, columns=headers)
air
Year Month Monthly_Anomaly Monthly_Unc. Annual_Anomaly Annual_Unc. Five-year_Anomaly Five-year_Unc. Ten-year_Anomaly Ten-year_Unc. Twenty-year_Anomaly Twenty-year_Unc.
0 1850 1 -0.777 0.412 NaN NaN NaN NaN NaN NaN NaN NaN
1 1850 2 -0.239 0.458 NaN NaN NaN NaN NaN NaN NaN NaN
2 1850 3 -0.426 0.447 NaN NaN NaN NaN NaN NaN NaN NaN
h2o
Year Month Monthly_Anomaly Monthly_Unc. Annual_Anomaly Annual_Unc. Five-year_Anomaly Five-year_Unc. Ten-year_Anomaly Ten-year_Unc. Twenty-year_Anomaly Twenty-year_Unc.
0 1850 1 -0.724 0.370 NaN NaN NaN NaN NaN NaN NaN NaN
1 1850 2 -0.221 0.430 NaN NaN NaN NaN NaN NaN NaN NaN
2 1850 3 -0.443 0.419 NaN NaN NaN NaN NaN NaN NaN NaN
My CSV has only two columns like:
I have multiple CSVs that have this format and tried to concatenate all of them using this:
path = r'...'
all_files = glob.glob(path + '/*.csv')
df_from_each_file = [pd.read_csv(f, error_bad_lines=False) for f in all_files]
concatenated_df = pd.concat(df_from_each_file, ignore_index=True)
However, this gives me
a a.1 a.2 command description
0 NaN NaN NaN aaa ikegroup WORD Name of the IKE group
1 NaN NaN NaN aaa ikegroup <cr> NaN
2 NaN NaN NaN aaa locald trace Show trace data for the locald component(cisco...
3 NaN NaN NaN aaa login trace Show trace data for login sub system
4 NaN NaN NaN aaa password-policy statistics Show statistics related to password policy
and I don't know where the first three columns came from.
What am I doing wrong?
I want to seperate the values of a dictionary into multiple columns and still be able to plot them. At this moment all the values are in one column.
So concretely I would like to split all the different values in the list of values. And use the amount of values in the longest list as the amount of columns. So for all the shorter lists I would like to fill in the gaps with something like 'NA' so I can still plot it in seaborn.
This is the dictionary that I used:
dictio = {'seq_7009': [6236.9764, 6367.049999999999], 'seq_418': [3716.3642000000004, 3796.4124000000006], 'seq_9143_unamb': [4631.958999999999], 'seq_2888': [5219.3359, 5365.4089], 'seq_1101': [4287.7417, 4422.8254], 'seq_107': [5825.695099999999, 5972.8073], 'seq_6946': [5179.3118, 5364.420900000001], 'seq_6162': [5531.503199999999, 5645.577399999999], 'seq_504': [4556.920899999999, 4631.959], 'seq_3535': [3396.1715999999997, 3446.1969999999997, 5655.896546], 'seq_4077': [4551.9108, 4754.0073,4565.987654,5668.9999976], 'seq_1626_unamb': [3724.3894999999998]}
This is the code for the dataframe:
df = pd.Series(dictio)
test=pd.DataFrame({'ID':df.index, 'Value':df.values})
seq_107 [5825.695099999999, 5972.8073]
seq_1101 [4287.7417, 4422.8254]
seq_1626_unamb [3724.3894999999998]
seq_2888 [5219.3359, 5365.4089]
seq_3535 [3396.1715999999997, 3446.1969999999997, 5655....
seq_4077 [4551.9108, 4754.0073, 4565.987654, 5668.9999976]
seq_418 [3716.3642000000004, 3796.4124000000006]
seq_504 [4556.920899999999, 4631.959]
seq_6162 [5531.503199999999, 5645.577399999999]
seq_6946 [5179.3118, 5364.420900000001]
seq_7009 [6236.9764, 6367.049999999999]
seq_9143_unamb [4631.958999999999]
Thanks in advance for the help!
Convert the Value column to a list of lists, and reload it into a new dataframe. Afterwards, call plot. Something like this -
df = pd.DataFrame(test.Value.tolist(), index=test.ID)
df
0 1 2 3
ID
seq_107 5825.6951 5972.8073 NaN NaN
seq_1101 4287.7417 4422.8254 NaN NaN
seq_1626_unamb 3724.3895 NaN NaN NaN
seq_2888 5219.3359 5365.4089 NaN NaN
seq_3535 3396.1716 3446.1970 5655.896546 NaN
seq_4077 4551.9108 4754.0073 4565.987654 5668.999998
seq_418 3716.3642 3796.4124 NaN NaN
seq_504 4556.9209 4631.9590 NaN NaN
seq_6162 5531.5032 5645.5774 NaN NaN
seq_6946 5179.3118 5364.4209 NaN NaN
seq_7009 6236.9764 6367.0500 NaN NaN
seq_9143_unamb 4631.9590 NaN NaN NaN
df.plot()