pandas add dataframe to the bottom of another code example

Example 1: pandas add dataframe to the bottom of another

# Basic syntax:
new_dataframe = old_dataframe.filter(['Columns','you','want'], axis=1)

Example 2: python count the number of zeros in each row of a pandas dataframe

# Basic syntax:
(pandas_dataframe == 0).sum(axis=1)
# Where axis 1 specifies that sum will operate on rows. Use 0 for columns

# Example usage:
# Create Pandas dataframe:
import pandas as pd
pandas_dataframe = pd.DataFrame({'a':[1,0,0,1,3], 
                                 'b':[0,0,1,0,1], 
                                 'c':[0,0,0,0,0]})
	a	b	c
0	1	0	0
1	0	0	0
2	0	1	0
3	1	0	0
4	3	1	0

(pandas_dataframe == 0).sum(axis=1)
0    2
1    3
2    2
3    2
4    1

Example 3: python seaborn violin plot fit data better

# Short answer:
# Adjust the bandwidth parameter to smaller values. E.g. bw = 0.1

# Example usage:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

data = np.random.rand(100)
sns.violinplot(y=data, bw=0.1) # Changing the bw parameter adjusts how
#	tightly the data is fit by the kernel density estimate (KDE)

Example 4: r return index of rows that have NA in dataframe

# Basic syntax:
which(is.na(your_dataframe), arr.ind=TRUE)
# Where:
#	- which returns the dataframe row indices for rows that contain
#		a logical of TRUE
#	- is.na processes the dataframe and converts all values to TRUE or 
#		FALSE based on whether they are NA or not

Example 5: python obtain data from pandas dataframe without index name

# Basic syntax (use index = False):
df.to_string(index = False)

Example 6: pandas add dataframe to the bottom of another

# Basic syntax:
import pandas as pd
appended_dataframe = dataframe_1.append(dataframe_2)
# or:
appended_dataframe = pd.concat([dataframe_1, dataframe_2]) 

# Example usage:
dataframe_1 = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
dataframe_2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
appended_dataframe = dataframe_1.append(dataframe_2)
print(appended_dataframe)
   A  B
0  1  2
1  3  4
0  5  6
1  7  8

# Note, add "ignore_index = False" if you want new sequential row indices
# Note, append does not modify the dataframes in place, which is why
#	running just dataframe_1.append(dataframe_2) doesn't change
#	dataframe_1
# Note, if the column names aren't the same, the dataframes will be
#	appended with NaNs like:
     A    B    C    D
0  1.0  2.0  NaN  NaN
1  3.0  4.0  NaN  NaN
0  NaN  NaN  5.0  6.0
1  NaN  NaN  7.0  8.0