I have a data frame like this:
d = {'col1': [1, 2, 4], 'col2': ["B", "A", "B"]}
df = pd.DataFrame(data=d)
I couldn't find a simple way to use dcc.Dropdown to filter the df for col2 (options A or B in drop down) and display the filtered df in the front end. Any help is really appreciated.
There's several questions all wrapped up together here, but the basic idea will be to build the dropdown with options based on the unique col2 values. Then you'll need a callback that listens to the dropdown's value and uses that to index the dataframe with something like df[df['col2'] == dropdown_value] where dropdown_value is the arg in the callback.
Related
I have a Google Sheet that has a cell that I want to use as a drop-down filter for another range within the sheet.
You can take a look at the sheet HERE.
Cell G11 is the cell that I wish to have the drop-down filter in.
Range B15:H69 is the range that should be filtered upon using this filter.
So, for example - if I select 'Barbarian' in the G11 drop-down the sheet should go from something like this (cut short for size of post sake):
To something like this:
I've tried using Named Ranges, using a seperate sheet to filter the data, and a couple other methods I found. Nothing seems to work exactly how I want it to, like in picture #2.
try:
=INDEX(LAMBDA(x, SPLIT(FLATTEN(SPLIT(FLATTEN(QUERY(TRANSPOSE(x),,9^9))&"×", "×")), " "))
(QUERY({ROW(Data!A2:D), SORT(Data!A2:D, 3, 0)},
"select Col1,Col2,'♀',Col3,Col4,'♂',Col5 where Col3 = '"&G11&"' label'♀''','♂'''")))
UPDATE:
=INDEX(LAMBDA(x, SPLIT(FLATTEN(SPLIT(FLATTEN(QUERY(TRANSPOSE(x),,9^9))&"×", "×")), " "))
(QUERY({ROW(Data!A2:D)-1, SORT(Data!A2:D, 3, 0)},
"select Col1,Col2,'♀',Col3,Col4,'♂',Col5 where 2=2 "&IF(
REGEXMATCH(G11, "(?i)all|n\/a|^$"),," and Col3 = '"&G11&"'")&" label'♀''','♂'''")))
I have an ordered dictionary which has 4 keys and multiple values. I tried to create the dataframe like this
df = pd.DataFrame(items, index=[0])
print('\ndf is ',df)
But this triggers ValueError, as the multiple values from the dictionary don't match.
The ordered dictionary is below:
OrderedDict([('Product', 'DASXZSDASXZS'), ('Region', ['A', 'B', 'C']), ('Items', ['1', '2', '3']), ('Order', ['123', '456', '789'])])
I want the dataframe format to be like:
Product Region Items Order
DASXZSDASXZS A 1 123
DASXZSDASXZS B 2 456
...
How can I achieve this format for the dataframe?
Not enough rep to comment. Why do you try to specify index=[0]?
Simply doing
df = pd.DataFrame(items)
works; if you want to change the index, you can set it later with df.set_index(...)
#viktor_dmitry your comment to #Battleman links to external data, here's a solution.
In https://www.codepile.net/pile/GY336DYN you have a list of OrderedDict entries, in the example above you just had 1 OrderedDict. Each needs to be treated as a separate DataFrame construction. From the resulting list you use concat to get a final DataFrame:
ods = [OrderedDict([('MaterialNumber', '2XV9450-1AR24'), ('ForCountry'...]),
OrderedDict([('MaterialNumber', ...),
...]
new_df = pd.concat([pd.DataFrame(od) for od in ods])
# new_df has 4 columns and many rows
Note also that 1 of your example items is invalid, you'd need to filter this out, the rest appear to be fine:
ods[21]
OrderedDict([('MaterialNumber', '4MC9672')]) # lacks the rest of the columns!
I'm trying to do something similar to this question: Google sheets using Filter and Sort together
I have an input range with two columns, and as output I want to create a dynamically sorted and filtered list from the input range, using a single formula.
See this document for the desired result: https://docs.google.com/spreadsheets/d/109xcbORFZxTjH0Vjd6PVqYlOxMIdK7aXqf5-jnMMPik/edit?usp=sharing
I tried the formula: =SORT(FILTER(B11:C100, B11:B100 = or(I11,I12,I13,I14)), 2, 0) but it doesn't work. What I am doing wrong here? Any help much appreciated.
try:
=ARRAYFORMULA(QUERY(B11:C,
"where lower(B) matches '"&TEXTJOIN("|", 1, LOWER(I11:I))&"'
order by C desc", 0))
You can modified or(,,,,) like follow:
=SORT(
FILTER(B11:C100,
((B11:B100 = I11)*1+(B11:B100 = I12)*1+(B11:B100 = I13)*1+(B11:B100 = I14)*1)>0
)
, 2, 0
)
So I have a pandas DataFrame, df, with columns that represent taxonomical classification (i.e. Kingdom, Phylum, Class etc...) I also have a list of taxonomic labels that correspond to the order I would like the DataFrame to be ordered by.
The list looks something like this:
class_list=['Gammaproteobacteria', 'Bacteroidetes', 'Negativicutes', 'Clostridia', 'Bacilli', 'Actinobacteria', 'Betaproteobacteria', 'delta/epsilon subdivisions', 'Synergistia', 'Mollicutes', 'Nitrospira', 'Spirochaetia', 'Thermotogae', 'Aquificae', 'Fimbriimonas', 'Gemmatimonadetes', 'Dehalococcoidia', 'Oscillatoriophycideae', 'Chlamydiae', 'Nostocales', 'Thermodesulfobacteria', 'Erysipelotrichia', 'Chlorobi', 'Deinococci']
This list would correspond to the Dataframe column df['Class']. I would like to sort all the rows for the whole dataframe based on the order of the list as df['Class'] is in a different order currently. What would be the best way to do this?
You could make the Class column your index column
df = df.set_index('Class')
and then use df.loc to reindex the DataFrame with class_list:
df.loc[class_list]
Minimal example:
>>> df = pd.DataFrame({'Class': ['Gammaproteobacteria', 'Bacteroidetes', 'Negativicutes'], 'Number': [3, 5, 6]})
>>> df
Class Number
0 Gammaproteobacteria 3
1 Bacteroidetes 5
2 Negativicutes 6
>>> df = df.set_index('Class')
>>> df.loc[['Bacteroidetes', 'Negativicutes', 'Gammaproteobacteria']]
Number
Bacteroidetes 5
Negativicutes 6
Gammaproteobacteria 3
Alex's solution doesn't work if your original dataframe does not contain all of the elements in the ordered list i.e.: if your input data at some point in time does not contain "Negativicutes", this script will fail. One way to get past this is to append your df's in a list and concatenate them at the end. For example:
ordered_classes = ['Bacteroidetes', 'Negativicutes', 'Gammaproteobacteria']
df_list = []
for i in ordered_classes:
df_list.append(df[df['Class']==i])
ordered_df = pd.concat(df_list)
I have a set of rows which I've fetched from a table. Let's say the object Rating. After fetching this object, I have say 100 entries from the database.
The Rating object might look like this:
table_ratings
t.integer :num
So what I now want to do is perform some calculations on these 100 rows without performing any other queries. I can do this, running an additional query:
r = Rating.all
good = r.where('num = 2') # this runs an additional query
"#{good}/#{r.length}"
This is a very rough idea of what I want to do, and I have other more complex output to build. Let's imagine I have over 10 different calculations I need to perform on these rows, and so aliasing these in a sql query might not be the best idea.
What would be an efficient way to replace the r.where query above? Is there a Ruby method or a gem which would give me a similar query api into ActiveRecord Association collection objects?
Rating.all returns an array of all Rating objects. From there, shift your focus to selecting and mapping from the array. eg.:
#perfect_ratings = r.select{|x| x.a == 100}
See:
http://www.ruby-doc.org/core-1.9.3/Array.html
ADDITIONAL COMMENTS:
Going over the list of methods available for array, I find myself using the following frequently:
To check a variable against multiple values:
%w[dog cat llama].include? #pet_type # returns true if #pet_type == 'cat'
To create another array(map and collect are aliases):
%w[dog cat llama].map(|pet| pet.capitalize) # ["Dog", "Cat", "Llama"]
To sort and drop duplicates:
%w[dog cat llama dog].sort.uniq # ["cat", "dog", "llama"]
<< to add an element, + to add arrays, flatten to flatten embedded arrays into a single level array, count or length or size for number of elements, and join are the others I tend to use a lot.
Finally, here is an example of join:
%w[dog cat llama].join(' or ') # "dog or cat or llama"