Making a pandas dataframe from a .npy file - python

I'm trying to make a pandas dataframe from a .npy file which, when read in using np.load, returns a numpy array containing a dictionary. My initial instinct was to extract the dictionary and then create a dataframe using pd.from_dict, but this fails every time because I can't seem to get the dictionary out of the array returned from np.load. It looks like it's just np.array([dictionary, dtype=object]), but I can't get the dictionary by indexing the array or anything like that. I've also tried using np.load('filename').item() but the result still isn't recognized by pandas as a dictionary.
Alternatively, I tried pd.read_pickle and that didn't work either.
How can I get this .npy dictionary into my dataframe? Here's the code that keeps failing...
import pandas as pd
import numpy as np
import os
targetdir = '../test_dir/'
filenames = []
successful = []
unsuccessful = []
for dirs, subdirs, files in os.walk(targetdir):
for name in files:
filenames.append(name)
path_to_use = os.path.join(dirs, name)
if path_to_use.endswith('.npy'):
try:
file_dict = np.load(path_to_use).item()
df = pd.from_dict(file_dict)
#df = pd.read_pickle(path_to_use)
successful.append(path_to_use)
except:
unsuccessful.append(path_to_use)
continue
print str(len(successful)) + " files were loaded successfully!"
print "The following files were not loaded:"
for item in unsuccessful:
print item + "\n"
print df


Let's assume once you load the .npy, the item (np.load(path_to_use).item()) looks similar to this;
{'user_c': 'id_003', 'user_a': 'id_001', 'user_b': 'id_002'}
So, if you need to come up with a DataFrame like below using above dictionary;
user_name user_id
0 user_c id_003
1 user_a id_001
2 user_b id_002
You can use;
df = pd.DataFrame(list(x.item().iteritems()), columns=['user_name','user_id'])
If you have a list of dictionaries like below;
users = [{'u_name': 'user_a', 'u_id': 'id_001'}, {'u_name': 'user_b', 'u_id': 'id_002'}]
You can simply use
df = pd.DataFrame(users)
To come up with a DataFrame similar to;
u_id u_name
0 id_001 user_a
1 id_002 user_b
Seems like you have a dictionary similar to this;
data = {
'Center': [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
'Vpeak': [1.1, 2.2],
'ID': ['id_001', 'id_002']
}
In this case, you can simply use;
df = pd.DataFrame(data) # df = pd.DataFrame(file_dict.item()) in your case
To come up with a DataFrame similar to;
Center ID Vpeak
0 [0.1, 0.2, 0.3] id_001 1.1
1 [0.4, 0.5, 0.6] id_002 2.2
If you have ndarray within the dict, do some preprocessing similar to below; and use it to create the df;
for key in data:
if isinstance(data[key], np.ndarray):
data[key] = data[key].tolist()
df = pd.DataFrame(data)

Related

How do I capture the properties I want from a string?

I hope you are well I have the following string:
"{\"code\":0,\"description\":\"Done\",\"response\":{\"id\":\"8-717-2346\",\"idType\":\"CIP\",\"suscriptionId\":\"92118213\"},....\"childProducts\":[]}}"...
To which I'm trying to capture the attributes: id, idType and subscriptionId and map them as a dataframe, but the entire body of the .cvs puts it in a single row so it is almost impossible for me to work without index
desired output:
id, idType, suscriptionID
0. '7-84-1811', 'CIP', 21312421412
1. '1-232-42', 'IO' , 21421e324
My code:
import pandas as pd
import json
path = '/example.csv'
df = pd.read_csv(path)
normalize_df = json.load(df)
print(df)

Considering your string is in JSON format, you can do this.
drop columns, transpose, and get headers right.
toEscape = "{\"code\":0,\"description\":\"Done\",\"response\":{\"id\":\"8-717-2346\",\"idType\":\"CIP\",\"suscriptionId\":\"92118213\"}}"
json_string = toEscape.encode('utf-8').decode('unicode_escape')
df = pd.read_json(json_string)
df = df.drop(["code","description"], axis=1)
df = df.transpose().reset_index().drop("index", axis=1)
df.to_csv("user_details.csv")
the output looks like this:
id idType suscriptionId
0 8-717-2346 CIP 92118213
Thank you for the question.

How to select multiple columns after applying group by a column

I'm trying to group by "sender" column and extract some related columns.Here is part of my dataset:
row number,type,rcvTime,sender,pos_x,pos_y,pos_z,spd_x,spd_y,spd_z,acl_x,acl_y,acl_z,hed_x,hed_y,hed_z
0,2,25207.0,15,136.07,1118.46,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-1.0,0.0
1,2,25208.0,15,136.19,1117.14,0.0,0.22,-2.31,0.0,0.14,-1.48,0.0,0.09,-1.0,0.0
2,3,25208.81,21,152.66,904.56,0.0,0.06,-0.75,0.0,0.18,-2.43,0.0,0.07,-1.0,0.0
3,2,25209.0,15,136.69,1113.79,0.0,0.39,-4.18,0.0,0.15,-1.64,0.0,0.09,-1.0,0.0
4,3,25209.81,21,152.98,902.59,0.0,0.22,-2.91,0.0,0.12,-1.68,0.0,0.07,-1.0,0.0
5,2,25210.0,15,133.77,1108.01,0.0,0.58,-6.17,0.0,0.16,-1.76,0.0,0.09,-1.0,0.0
6,3,25210.81,21,153.25,898.68,0.0,0.37,-4.65,0.0,0.11,-1.35,0.0,0.08,-1.0,0.0
7,2,25211.0,15,134.37,1100.75,0.0,0.76,-8.14,0.0,0.18,-1.93,0.0,0.09,-1.0,0.0
8,3,25211.81,21,153.82,893.0,0.0,0.65,-6.67,0.0,0.25,-2.54,0.0,0.1,-1.0,0.0
9,3,25211.93,27,122.87,892.12,0.0,5.63,0.32,0.0,-1.57,-0.09,0.0,1.0,0.04,0.0
Here is what I have tried and the result is just all the 'rcvTime' data for that sender However I need all other columns like pos_x,spd_x as well:
import numpy as np
import pandas as pd
df = pd.read_csv(r"/Users/h/trace.csv")
df.head()
df1 = df.groupby('sender')['rcvTime'].apply(list).reset_index(name='new')
print(df1)
What I need is the following data, I just wrote for sender=15:
rowNumber,sender,rcvTime,pos_x,spd_x,rcvTime,pos_x,spd_x,rcvTime,pos_x,spd_x,...
0,15,25207.0,136.07,0.0,25208.0,136.19,0.22, 25209.0,... 25210.0,..., 25211.0, ...
1,21,25208.81,152.66,0.06, 25209.81,..., 25210.81,..., 25211.81,..., 25212...
2,27,25211.93..., 25212.93..., 25213.93..., 25214.93..., 25215...

IIUC you search for something like this:
df1 = df.groupby('sender',as_index=False).agg(lambda x: list(x))
EDIT
I'm sure there is a better way, but here how I managed to achieve your desired output:
cols = ['rcvTime', 'pos_x', 'spd_x']
grouped = df.groupby('sender')[cols]
list_of_lists = [tup[1].values.flatten().tolist() for tup in grouped.pipe(list)]
res = pd.DataFrame({'sender': grouped.groups.keys(), f'{cols*len(grouped.groups.keys())}' : list_of_lists})
print(res)
sender ['rcvTime', 'pos_x', 'spd_x', 'rcvTime', 'pos_x', 'spd_x', 'rcvTime', 'pos_x', 'spd_x']
0 15 [25207.0, 136.07, 0.0, 25208.0, 136.19, 0.22, ...
1 21 [25208.81, 152.66, 0.06, 25209.81, 152.98, 0.2...
2 27 [25211.93, 122.87, 5.63]
Still think, you don't benefit of the possibilities with pandas when formatting your data like this.

Pandas DF.output write to columns (current data is written all to one row or one column)

I am using Selenium to extract data from the HTML body of a webpage and am writing the data to a .csv file using pandas.
The data is extracted and written to the file, however I would like to manipulate the formatting of the data to write to specified columns, after reading many threads and docs I am not able to understand how to do this.
The current CSV file output is as follows, all data in one row or one column
0,
B09KBFH6HM,
dropdownAvailable,
90,
1,
B09KBNJ4F1,
dropdownAvailable,
100,
2,
B09KBPFPCL,
dropdownAvailable,
110
or if I use the [count] count +=1 method it will be one row
0,B09KBFH6HM,dropdownAvailable,90,1,B09KBNJ4F1,dropdownAvailable,100,2,B09KBPFPCL,dropdownAvailable,110
I would like the output to be formatted as follows,
/col1 /col2 /col3 /col4
0, B09KBFH6HM, dropdownAvailable, 90,
1, B09KBNJ4F1, dropdownAvailable, 100,
2, B09KBPFPCL, dropdownAvailable, 110
I have tried using columns= options but get errors in the terminal and don't understand what feature I should be using to achieve this in the docs under the append details
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.append.html?highlight=append#pandas.DataFrame.append
A simplified version is as follows
from selenium import webdriver
import pandas as pd
price = []
driver = webdriver.Chrome("./chromedriver")
driver.get("https://www.example.co.jp/dp/zzzzzzzzzz/")
select_box = driver.find_element_by_name("dropdown_selected_size_name")
options = [x for x in select_box.find_elements_by_tag_name("option")]
for element in options:
price.append(element.get_attribute("value"))
price.append(element.get_attribute("class"))
price.append(element.get_attribute("data-a-html-content"))
output = pd.DataFrame(price)
output.to_csv("Data.csv", encoding='utf-8-sig')
driver.close()
Do I need to parse each item separately and append?
I would like each of the .get_attribute values to be written to a new column.
Is there any advice you can offer for a solution to this as I am not very proficient at pandas, thank you for your helps

 Approach similar to #user17242583, but a little shorter:
data = [[e.get_attribute("value"), e.get_attribute("class"), e.get_attribute("data-a-html-content")] for e in options]
df = pd.DataFrame(data, columns=['ASIN', 'dropdownAvailable', 'size']) # third column maybe is the product size
df.to_csv("Data.csv", encoding='utf-8-sig')

Adding all your items to the price list is going to cause them all to be in one column. Instead, store separate lists for each column, in a dict, like this (name them whatever you want):
data = {
'values': [],
'classes': [],
'data_a_html_contents': [],
}
...
for element in options:
values.append(element.get_attribute("value"))
classes.append(element.get_attribute("class"))
data_a_html_contents.append(element.get_attribute("data-a-html-content"))
...
output = pd.DataFrame(data)
output.to_csv("Data.csv", encoding='utf-8-sig')

You were collecting the value, class and data-a-html-content and appending them to the same list price. Hence, the list becomes:
price = [value1, class1, data-a-html-content1, value2, class2, data-a-html-content2, ...]
Hence, within the dataframe it looks like:
Solution
To get value, class and data-a-html-content in seperate columns you can adopt any of the below two approaches:
Pass a dictionary to the dataframe.
Pass a list of lists to the dataframe.
While the #user17242583 and #h.devillefletcher suggests a dictionary, you can still achieve the same using list of lists as follows:
values = []
classes = []
data-a-html-contents = []
driver = webdriver.Chrome("./chromedriver")
driver.get("https://www.example.co.jp/dp/zzzzzzzzzz/")
select_box = driver.find_element_by_name("dropdown_selected_size_name")
options = [x for x in select_box.find_elements_by_tag_name("option")]
for element in options:
values.append(element.get_attribute("value"))
classes.append(element.get_attribute("class"))
data-a-html-contents.append(element.get_attribute("data-a-html-content"))
df = pd.DataFrame(data=list(zip(values, classes, data-a-html-contents)), columns=['Value', 'Class', 'Data-a-Html-Content'])
output = pd.DataFrame(my_list)
output.to_csv("Data.csv", encoding='utf-8-sig')
References
You can find a couple of relevant detailed discussions in:
Selenium: Web-Scraping Historical Data from Coincodex and transform into a Pandas Dataframe
Python Selenium: How do I print the values from a website in a text file?

Creating Dataframe with JSON Keys

I have a JSON file which resulted from YouTube's iframe API and I want to put this JSON data into a pandas dataframe, where each JSON key will be a column, and each record should be a new row.
Normally I would use a loop and iterate over the rows of the JSON but this particular JSON looks like this :
[
"{\"timemillis\":1563467467703,\"date\":\"18.7.2019\",\"time\":\"18:31:07,703\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:02\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.3,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}",
"{\"timemillis\":1563467468705,\"date\":\"18.7.2019\",\"time\":\"18:31:08,705\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:03\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.5,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}"
]
In this JSON not every key is written as a new line. How can I extract the keys in this case, and express them as columns?

A Pythonic Solution would be to use the keys and values API of the Python Dictionary.
it should be something like this:
ls = [
"{\"timemillis\":1563467467703,\"date\":\"18.7.2019\",\"time\":\"18:31:07,703\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:02\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.3,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}",
"{\"timemillis\":1563467468705,\"date\":\"18.7.2019\",\"time\":\"18:31:08,705\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:03\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.5,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}"
]
ls = [json.loads(j) for j in ls]
keys = [j.keys() for j in ls] # this will get you all the keys
vals = [j.values() for j in ls] # this will get the values and then you can do something with it
print(keys)
print(values)

easiest way is to leverage json_normalize from pandas.
import json
from pandas.io.json import json_normalize
input_dict = [
"{\"timemillis\":1563467467703,\"date\":\"18.7.2019\",\"time\":\"18:31:07,703\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:02\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.3,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}",
"{\"timemillis\":1563467468705,\"date\":\"18.7.2019\",\"time\":\"18:31:08,705\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:03\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.5,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}"
]
input_json = [json.loads(j) for j in input_dict]
df = json_normalize(input_json)

I think you are asking to break down your key and values and want keys as a column,and values as a row:
This is my approach and plz always provide how your expected output should like
ChainMap flats your dict in key and values and pretty much is self explanatory.
data = ["{\"timemillis\":1563467467703,\"date\":\"18.7.2019\",\"time\":\"18:31:07,703\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:02\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.3,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}","{\"timemillis\":1563467468705,\"date\":\"18.7.2019\",\"time\":\"18:31:08,705\",\"videoId\":\"0HJx2JhQKQk\",\"startSecond\":\"0\",\"stopSecond\":\"90\",\"playerStateNumeric\":1,\"playerStateVerbose\":\"Playing\",\"curTimeFormatted\":\"0:03\",\"totalTimeFormatted\":\"9:46\",\"playoutLevelPercent\":0.5,\"bufferLevelPercent\":1.4,\"qual\":\"large\",\"qualLevels\":[\"hd720\",\"large\",\"medium\",\"small\",\"tiny\",\"auto\"],\"playbackRate\":1,\"playbackRates\":[0.25,0.5,0.75,1,1.25,1.5,1.75,2],\"playerErrorNumeric\":\"\",\"playerErrorVerbose\":\"\"}"]
import json
from collections import ChainMap
data = [json.loads(i) for i in data]
data = dict(ChainMap(*data))
keys = []
vals = []
for k,v in data.items():
keys.append(k)
vals.append(v)
data = pd.DataFrame(zip(keys,vals)).T
new_header = data.iloc[0]
data = data[1:]
data.columns = new_header
#startSecond playbackRates playbackRate qual totalTimeFormatted timemillis playerStateNumeric playerStateVerbose playerErrorNumeric date time stopSecond bufferLevelPercent playerErrorVerbose qualLevels videoId curTimeFormatted playoutLevelPercent
#0 [0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2] 1 large 9:46 1563467467703 1 Playing 18.7.2019 18:31:07,703 90 1.4 [hd720, large, medium, small, tiny, auto] 0HJx2JhQKQk 0:02 0.3

Create a csv file from multiple dictionaries?

I'm calculating the frequency of words into many text files (140 docs), the end of my work is to create a csv file where I can order the frequence of every word by single doc and by all docs.
Let say I have:
absolut_freq= {u'hello':0.001, u'world':0.002, u'baby':0.005}
doc_1= {u'hello':0.8, u'world':0.9, u'baby':0.7}
doc_2= {u'hello':0.2, u'world':0.3, u'baby':0.6}
...
doc_140={u'hello':0.1, u'world':0.5, u'baby':0.9}
So, what I need is a cvs file to export in excel that looks like this:
WORD, ABS_FREQ, DOC_1_FREQ, DOC_2_FREQ, ..., DOC_140_FREQ
hello, 0.001 0.8 0.2 0.1
world, 0.002 0.9 0.03 0.5
baby, 0.005 0.7 0.6 0.9
How can I do It with python?

You could also convert it to a Pandas Dataframe and save it as a csv file or continue analysis in a clean format.
absolut_freq= {u'hello':0.001, u'world':0.002, u'baby':0.005}
doc_1= {u'hello':0.8, u'world':0.9, u'baby':0.7}
doc_2= {u'hello':0.2, u'world':0.3, u'baby':0.6}
doc_140={u'hello':0.1, u'world':0.5, u'baby':0.9}
all = [absolut_freq, doc_1, doc_2, doc_140]
# if you have a bunch of docs, you could use enumerate and then format the colname as you iterate over and create the dataframe
colnames = ['AbsoluteFreq', 'Doc1', 'Doc2', 'Doc140']
import pandas as pd
masterdf = pd.DataFrame()
for i in all:
df = pd.DataFrame([i]).T
masterdf = pd.concat([masterdf, df], axis=1)
# assign the column names
masterdf.columns = colnames
# get a glimpse of what the data frame looks like
masterdf.head()
# save to csv
masterdf.to_csv('docmatrix.csv', index=True)
# and to sort the dataframe by frequency
masterdf.sort(['AbsoluteFreq'])

You can make it a mostly a data-driven process—given only the variable names of all the dictionary variables—by first creating a table with all the data listed in it, and then using the csv module to write a transposed (columns for rows swapped) version it to the output file.
import csv
absolut_freq = {u'hello': 0.001, u'world': 0.002, u'baby': 0.005}
doc_1 = {u'hello': 0.8, u'world': 0.9, u'baby': 0.7}
doc_2 = {u'hello': 0.2, u'world': 0.3, u'baby': 0.6}
doc_140 ={u'hello': 0.1, u'world': 0.5, u'baby': 0.9}
dic_names = ('absolut_freq', 'doc_1', 'doc_2', 'doc_140') # dict variable names
namespace = globals()
words = namespace[dic_names[0]].keys() # assume dicts all contain the same words
table = [['WORD'] + list(words)] # header row (becomes first column of output)
for dic_name in dic_names: # add values from each dictionary given its name
table.append([dic_name.upper()+'_FREQ'] + list(namespace[dic_name].values()))
# Use open('merged_dicts.csv', 'wb') for Python 2.
with open('merged_dicts.csv', 'w', newline='') as csvfile:
csv.writer(csvfile).writerows(zip(*table))
print('done')
CSV file produced:
WORD,ABSOLUT_FREQ_FREQ,DOC_1_FREQ,DOC_2_FREQ,DOC_140_FREQ
world,0.002,0.9,0.3,0.5
baby,0.005,0.7,0.6,0.9
hello,0.001,0.8,0.2,0.1

No matter how you want to write this data, first you need an ordered data structure, for example a 2D list:
docs = []
docs.append( {u'hello':0.001, u'world':0.002, u'baby':0.005} )
docs.append( {u'hello':0.8, u'world':0.9, u'baby':0.7} )
docs.append( {u'hello':0.2, u'world':0.3, u'baby':0.6} )
docs.append( {u'hello':0.1, u'world':0.5, u'baby':0.9} )
words = docs[0].keys()
result = [ [word] + [ doc[word] for doc in docs ] for word in words ]
then you can use the built-in csv module: https://docs.python.org/2/library/csv.html

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