I want my plot to retrieve data from one dataframe, but hovering over the data i want it to incorperate data from both data frames.
example:
which results from
fig = px.scatter(X_reduced_df, x='EXTRACTION_DATE_SAMPLE', y='score_IF', color= 'anomaly_class_IF', hover_data=['score_IF','delta',
'omicron',
'NH4-N (titr.)(W)_Zusatzparameter-ESRI',
'P ges. filtr. (photom.)(W) mA_Zusatzparameter-ESRI',
'BSB5 (mit Verd) DIN 1899_Zusatzparameter-ESRI',
'N org. (Ber. TKN)(W)_Zusatzparameter-ESRI'
], #range_x=['2015-12-01', '2016-01-15'],
title="Default Display with Gaps")
fig.show()
Here i want the value "delta" to be associated with additional info on "delta" from another dataframe, i.e. i want "delta= 0, add info" where add info is a list or a dataframecolumn, or similar.(its a list of names associated with a double, like as:
column name: delta
column entries
gamma: 1.2
alpha: 1.3
.
.
.
)
basically its a correlation matrix. and i want the correlations associated with each entry to be displayed.
The second dataframe, the correlation matrix is regrettably not the same columns as the original dataframe, hence not joinable. i want column names to be associated with the add info. i thought about categories, but i cannot see how that could help for a compact add info.
also i do not want to meddle with the column names(like in forcing a rename with the add info).
the plotly library only allows for one dataframe as input, right? how can i add my add info, the i way i described?
Related
I have a dataframe with multiple columns such as product name, reviews, origin, and etc.
Here, I want to create a barplot with only the data from "Origin" column.
To do this, I used the code:
origin = df['Origin'].value_counts()
With this, I was able to get a list of countries with corresponding frequencies (or counts). Now, I want to create a boxplot with each country on X-axis and counted frequencies on the Y-axis. Although the column for frequencies have a column label, I am unable to set the X-axis as the countries are merely saved as index. Would there be a better way to count the column "Origin" and make it into a barplot?
Thanks in advance.
I have an excel file containing values, I needed values as the highlighted one in single column and deleting the rest on. But due to mismatch in rows and column header file, I am not able to extract. Once you will see the excel will able to understand what values I needed.As this is just a sample of mine data.
Column A2:A17 date is continuous but few date are repeating, but in Row (D1:K1) date are not repeating, so in this case value of same date occurring just below of of one other.
How to get values in one column?
Is there a way to highlight the values of same date occurring in row and column? The sample data consist of manually highlighted. I have huge dataset that cannot be manually highlighted.
Because from colour code also I can get the required values too.
Following is the file I am attaching here
https://docs.google.com/spreadsheets/d/1-xBMKRP1_toA_Ky8mKxCKAFi4uQ8YWJq/edit?usp=sharing&ouid=110042758694954349181&rtpof=true&sd=true
Please visit the link and help me to find the solution.
Thank you
I'm not clear what those values in columns D to K are.
If only the shaded ones matter and they can be derived from the Latitude and Longitude for each row separately:
Insert a column titled "Row", say in A, and populate it 1,2,3...
I think you also want a column E which is whatever the calculation you currently have in D-K. Is this "Distance"?
Then create a Pivot Table on rows A to E and you can do anything you are likely to need: https://support.microsoft.com/en-us/office/create-a-pivottable-to-analyze-worksheet-data-a9a84538-bfe9-40a9-a8e9-f99134456576
Dates at Colum Labels, Row numbers as Row Labels, and Sum of "Distance" as Values.
In the above image, I colored some rows with the same colors, so I want to create new data frames with the values of the same color. as you can see, the values of the same color are the same as an example - 0.8 multiple and CE option type values rows, in this entire data frame these same values come in 2 times, so I want to create these 3 rows new data frame, and like the same i want to do for all rows.
Below is some code that may help you.
df_dictionary = dict(tuple(your_dataframe.groupby('columns_to_groupby')))
This will produce a dictionary whose keys are the grouped values (in your case, "CE, PE, etc...") and whose values are the dataframes split by the grouping specified. Hope this helps.
I have a data frame called "df" with column = "date", "regions", "transactions". I want to plot the data frame in such a way so I can see transactions for only "selected regions" and not all the regions in my df.
For example- I want to see a plot with transactions for Regions = "a","X","z" only - all in the same graph - and "date" being my x-axis.
So far, I have been able to plot transactions data for all the regions in one graph but not able to slice my data for the regions that I want.
Can someone please help?
you can use df.loc to access only a group of rows or columns. Read below https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html
In your case, something like this would return the df with just the required regions
required_regions = ['a','X','z']
df.loc[df['regions'].isin(required_regions)]
I have excel data file with thousands of rows and columns.
I am using python and have started using pandas dataframes to analyze data.
What I want to do in column D is to calculate annual change for values in column C for each year for each ID.
I can use excel to do this – if the org ID is same are that in the prior row, calculate annual change (leaving the cells highlighted in blue because that’s the first period for that particular ID). I don’t know how to do this using python. Can anyone help?
Assuming the dataframe is already sorted
df.groupby(‘ID’).Cash.pct_change()
However, you can speed things up with the assumption things are sorted. Because it’s not necessary to group in order to calculate percentage change from one row to next
df.Cash.pct_change().mask(
df.ID != df.ID.shift()
)
These should produce the column values you are looking for. In order to add the column, you’ll need to assign to a column or create a new dataframe with the new column
df[‘AnnChange’] = df.groupby(‘ID’).Cash.pct_change()