pandas has a replace method too: In [25]: df = DataFrame({1: [2,3,4], 2: [3,4,5]}) In [26]: df Out[26]: 1 2 0 2 3 1 3 4 2 4 5 In [27]: df[2] Out[27]: 0 3 1 4 2 5 Name: 2 In [28]: df[2].replace(4, 17) Out[28]: 0 3 1 17 2 5 Name: 2 In … Read more
Pandas subplots=True will arange the axes in a single column. import numpy as np import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame(np.random.rand(7,20)) df.plot(subplots=True) plt.tight_layout() plt.show() Here, tight_layout isn’t applied, because the figure is too small to arange the axes nicely. One can use a bigger figure (figsize=(…)) though. In order to have … Read more
In order to bucket your series, you should use the pd.cut() function, like this: df[‘bin’] = pd.cut(df[‘1’], [0, 50, 100,200]) 0 1 file bin 0 person1 24 age.csv (0, 50] 1 person2 17 age.csv (0, 50] 2 person3 98 age.csv (50, 100] 3 person4 6 age.csv (0, 50] 4 person2 166 Height.csv (100, 200] 5 … Read more
If you use a list containing the column name(s) instead of a string, data.set_index will work The following should show the dates on x-axis: #! /usr/bin/env python import pandas as pd import matplotlib.pyplot as plt data = pd.read_fwf(‘myfile.log’,header=None,names=[‘time’,’amount’],widths=[27,5]) data.time = pd.to_datetime(data[‘time’], format=”%Y-%m-%d %H:%M:%S.%f”) data.set_index([‘time’],inplace=True) data.plot() #OR plt.plot(data.index, data.amount)
There is difference value_counts return: The resulting object will be in descending order so that the first element is the most frequently-occurring element. but count not, it sort output by index (created by column in groupby(‘col’)). df.groupby(‘colA’).count() is for aggregate all columns of df by function count. So it count values excluding NaNs. So if … Read more
Use sort_values, i.e. means = means.sort_values(). [Pandas v0.17+] (Very old answer, pre-v0.17 / 2015) pandas used to use order() method: means = means.order().
Use max with axis=1: df = df.max(axis=1) print (df) 0 2.0 1 3.2 2 8.8 3 7.8 dtype: float64 And if need new column: df[‘max_value’] = df.max(axis=1) print (df) a b c max_value 0 1.2 2.0 0.10 2.0 1 2.1 1.1 3.20 3.2 2 0.2 1.9 8.80 8.8 3 3.3 7.8 0.12 7.8
use astype(np.int64) s = pd.Series([”, 8.00735e+09, 4.35789e+09, 6.10644e+09]) mask = pd.to_numeric(s).notnull() s.loc[mask] = s.loc[mask].astype(np.int64) s 0 1 8007350000 2 4357890000 3 6106440000 dtype: object
diff should give the desired result: >>> df.diff() count_a count_b 2015-01-01 NaN NaN 2015-01-02 38465 NaN 2015-01-03 36714 NaN 2015-01-04 35137 NaN 2015-01-05 35864 NaN …. 2015-02-07 142390 25552 2015-02-08 126768 22835 2015-02-09 122324 21485
First, replace your empty cells with NaNs: df[df[0]==””] = np.NaN Now, Use ffill(): df.fillna(method=’ffill’) # 0 #0 Text #1 30 #2 30 #3 30 #4 31 #5 Text #6 31 #7 31 #8 31 #9 32