Given a dataset with the following structure: Given as a .csv: Note: some values are missing, not all variables are available for all locations, timestamps are available for every record, columns may appear out of order, but timestamp is reliably the first column. I’m not sure all these aspects are relevant to an optimal solution, but there they are. I
Tag: pandas
How to convert JSON data inside a pandas column into new columns
I have this short version of ADSB json data and would like to convert it into dataFrame columns as Icao, Alt, Lat, Long, Spd, Cou….. After Alperen told me to do this I can load it into a DataFrame. However, df.acList is [{‘Id’: 10537990, ‘Rcvr’: 1, ‘HasSig’: False, … Name: acList, dtype: object How can I get the Icao, Alt,
How can I extract the nth row of a pandas data frame as a pandas data frame?
Suppose a Pandas dataframe looks like: How can I extract the third row (as row3) as a pandas dataframe? In other words, row3.shape should be (1,5) and row3.head() should be: Answer Use .iloc with double brackets to extract a DataFrame, or single brackets to pull out a Series. This extends to other forms of DataFrame indexing as well, namely .loc
TypeError: expected string or bytes-like object – with Python/NLTK word_tokenize
I have a dataset with ~40 columns, and am using .apply(word_tokenize) on 5 of them like so: df[‘token_column’] = df.column.apply(word_tokenize). I’m getting a TypeError for only one of the columns, we’ll call this problem_column Here’s the full error (stripped df and column names, and pii), I’m new to Python and am still trying to figure out which parts of the
Sklearn logistic regression, plotting probability curve graph
I’m trying to create a logistic regression similar to the ISLR’s example, but using python instead But I keep getting the graph on the left, when I want the one on the right: Edit: plt.scatter(x,LogR.predict(x)) was my second, and also wrong guess. Answer you use predict(X) which gives out the prediction of the class. replace predict(X) with predict_proba(X)[:,1] which would
Wrong labels when plotting a time series pandas dataframe with matplotlib
I am working with a dataframe containing data of 1 week. I create a new index by combining the weekday and time i.e. The plot of this data is the following: The plot is correct as the labels reflect the data in the dataframe. However, when zooming in, the labels do not seem correct as they no longer correspond to
Python pandas cumsum with reset everytime there is a 0
I have a matrix with 0s and 1s, and want to do a cumsum on each column that resets to 0 whenever a zero is observed. For example, if we have the following: The result I desire is: However, when I try df.cumsum() * df, I am able to correctly identify the 0 elements, but the counter does not reset:
How do you read in a dataframe with lists using pd.read_clipboard?
Here’s some data from another question: What I would do first is to add quotes across all words, and then: Is there a smarter way to do this? Answer Lists of strings For basic structures you can use yaml without having to add quotes: Lists of numeric data Under certain conditions, you can read your lists as strings and the
plot multiple pandas dataframes in one graph
I have created 6 different dataframes that eliminate the outliers of their own original data frames. Now, I’m trying to plot all of the dataframes that eliminate the outliers on the same graph. This is my code that eliminates the outliers in each data frame: If I remove the comment newdf.plot() I will be able to plot all of the
Join/Merge two Pandas dataframes and use columns as multiindex
I have two dataframes with KPIs by date. I want to combine them and use multi-index so that each KPI can be easily compared to the other for the two df. Like this: I have tried to extract each KPI into a series, rename the series accordingly (df1, df2), and then concatenating them using the keys argument of pd.concat but