The plotting code assumes that each bar in a bar plot deserves its own label. You could override this assumption by specifying your own formatter: ax.xaxis.set_major_formatter(formatter) The pandas.tseries.converter.TimeSeries_DateFormatter that Pandas uses to format the dates in the “good” plot works well with line plots when the x-values are dates. However, with a bar plot the … Read more
You need astype: df[‘zipcode’] = df.zipcode.astype(str) #df.zipcode = df.zipcode.astype(str) For converting to categorical: df[‘zipcode’] = df.zipcode.astype(‘category’) #df.zipcode = df.zipcode.astype(‘category’) Another solution is Categorical: df[‘zipcode’] = pd.Categorical(df.zipcode) Sample with data: import pandas as pd df = pd.DataFrame({‘zipcode’: {17384: 98125, 2680: 98107, 722: 98005, 18754: 98109, 14554: 98155}, ‘bathrooms’: {17384: 1.5, 2680: 0.75, 722: 3.25, 18754: 1.0, … Read more
You can use pd.merge: import pandas as pd pd.merge(df1, df2, on=”movie_title”) Only rows are kept for which common keys are found in both data frames. In case you want to keep all rows from the left data frame and only add values from df2 where a matching key is available, you can use how=”left”: pd.merge(df1, … Read more
It’s actually pretty simple (FWIW, I originally thought to do it your way): df[‘bar’, ‘three’] = [0, 1, 2] df = df.sort_index(axis=1) print(df) bar baz one two three one two A -0.212901 0.503615 0 -1.660945 0.446778 B -0.803926 -0.417570 1 -0.336827 0.989343 C 3.400885 -0.214245 2 0.895745 1.011671
You need to set index=False in to_excel in order for it to not write the index column out, this semantic is followed in other Pandas IO tools, see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html and http://pandas.pydata.org/pandas-docs/stable/io.html
have you tried the display command? from IPython.display import display display(salaries.head()) display(teams.head())
You can use DataFrame.fillna or Series.fillna which will replace the Python object None, not the string ‘None’. import pandas as pd import numpy as np For dataframe: df = df.fillna(value=np.nan) For column or series: df.mycol.fillna(value=np.nan, inplace=True)
Debian based images use only python pip to install packages with .whl format: Downloading pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl (26.2MB) Downloading numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl (12.2MB) WHL format was developed as a quicker and more reliable method of installing Python software than re-building from source code every time. WHL files only have to be moved to the correct location on the target … Read more
You may use the swifter package: pip install swifter (Note that you may want to use this in a virtualenv to avoid version conflicts with installed dependencies.) Swifter works as a plugin for pandas, allowing you to reuse the apply function: import swifter def some_function(data): return data * 10 data[‘out’] = data[‘in’].swifter.apply(some_function) It will automatically … Read more
Numpy solution. We will shuffle the whole dataset first (df.sample(frac=1, random_state=42)) and then split our data set into the following parts: 60% – train set, 20% – validation set, 20% – test set In [305]: train, validate, test = \ np.split(df.sample(frac=1, random_state=42), [int(.6*len(df)), int(.8*len(df))]) In [306]: train Out[306]: A B C D E 0 0.046919 … Read more