Unnamed: 0 int64 0 378k | id int64 49.9k 73.8M | title stringlengths 15 150 | question stringlengths 37 64.2k | answer stringlengths 37 44.1k | tags stringlengths 5 106 | score int64 -10 5.87k |
|---|---|---|---|---|---|---|
351,600 | 70,642,617 | How do I split parts of a dataframe by a character? | <p>I have this kind of input:</p>
<pre><code>[["October", "Steve", "Apples"],
["November", "Joe", "Oranges"],
["December", "James", "Apples/Oranges"]
]
</code></pre>
<p>I would like to split James' two occurrences in to separate... | <p>Assuming this input:</p>
<pre><code> A B C
0 October Steve Apples
1 November Joe Oranges
2 December James Apples/Oranges
</code></pre>
<p>You could <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.str.split.html" rel="nofollow noreferrer"><cod... | python|pandas | 2 |
351,601 | 70,494,486 | Matplotlib Animation not completing | <p>I've tried to create an animation using Matplotlib, it seems to be animating correctly but it's not showing all the data points in the dataset. Its a time series from Jan 2020 to Dec 2021 - it seems to be animating to 2020-06 only right now.</p>
<p>Below is the entire code</p>
<pre><code>import yfinance as yf
import... | <p>The basic form of the animation is to set the empty graph type and the x-axis and y-axis ranges, and then update the data with the animation function. It is rewritten in an object-oriented way because it is easy to set up the details. The animation is set to repeat for a number of records in a number of frames.</p>
... | python|pandas|matplotlib | 1 |
351,602 | 70,582,759 | Filtering pandas series based on patterns | <p>Happy new year!</p>
<p>I have a quick question: Let's say I have a panda Series with 3 events like this</p>
<pre><code>myEvents = pd.Series(['up', 'down', 'None', 'None, 'up', 'down', 'down', 'up', 'up'])
</code></pre>
<p>I would like to keep the 'valid' events only: A valid event is an up followed by a down or a do... | <p>Algorithm:</p>
<ol>
<li>Remove all "None" rows from the series</li>
<li>Get a column of the previous row's value</li>
<li>Filter out rows where the current is equal to the previous row's value</li>
</ol>
<p>Code:</p>
<pre><code># 1)
df = pd.DataFrame(
{"my_events": my_events}
)
df = df[df["... | python|pandas|state-machine | 1 |
351,603 | 70,495,978 | Cluster values within two columns in groups in pandas | <p>I have a <code>dataframe</code> like this:</p>
<pre><code>VAL1 VAL2
A A
B B
E E
F F
G G
H H
I I
J J
A B
A C
B A
B C
C A
C B
D E
E D
F E
E F
G H
H G
I J
J I
I H
H I
K K
</code></pre>
<p>And I would like to cluster into <code>Groups</code> t... | <p>Create <a href="https://stackoverflow.com/a/53886179/2901002">connected_components</a> for list <code>L</code> and then convert to <code>DataFrame</code>:</p>
<pre><code>import networkx as nx
# Create the graph from the dataframe
g = nx.Graph()
g.add_edges_from(df[['VAL1','VAL2']].itertuples(index=False))
new = l... | python|python-3.x|pandas | 1 |
351,604 | 70,454,171 | pandas fill null values by the mean of that category (use loop?) | <p>I am trying to fill in the missing data in the data set based on the average of the values observed during that year, and it takes a long time to write one by one. I can't create this structure with a for loop. How should it be coded?</p>
<pre class="lang-py prettyprint-override"><code>df['TOTAL_REVENUE'] = df.TOTAL... | <p>You'd do it like this (use <code>df[column]</code> instead of <code>df.column</code>):</p>
<pre><code>for column in df.columns:
df[column] = df[column].fillna(df.groupby('YEAR')[column].transform('mean'))
</code></pre> | pandas|dataframe|pandas-groupby | 0 |
351,605 | 70,579,291 | Create new column using str.contains and based on if-else condition | <p>I have a list of names 'pattern' that I wish to match with strings in column 'url_text'. If there is a match i.e. <code>True</code> the name should be printed in a new column 'pol_names_block' and if <code>False</code> leave the row empty.</p>
<pre><code>pattern = '|'.join(pol_names_list)
print(pattern)
'Jon Kyl|D... | <p>Change your pattern to enclose it around a capture group <code>()</code> and use <code>extract</code>:</p>
<pre><code>pattern = fr"({'|'.join(pol_names_list)})"
df['pol_name_block'] = df['url_text'].str.extract(pattern)
print(df)
# Output <- with the sample of @tlentali
id url_text pol_name_... | python|pandas|lambda|apply | 2 |
351,606 | 70,579,088 | When running a loop using prange from numba to parallelize it, are elements appended in the same order? | <p>I am using numba prange to try to paralelize the following function:</p>
<pre><code>@njit(parallel=True)
def contrast_16bit(video):
n = video.shape[0]
#New max value
high = 65535
video16 = []
for i in prange(n):
data = video[i,:,:]
#Old max and min
vmin = np.mi... | <p>As for the literal question you're asking - no idea, and it's honestly hard to know without digging in the implementation, so I would honestly be scared to depend on the ordering of this parallelized operation.</p>
<p>However, you can sidestep the whole issue by allocating an array instead: <code>video16 = np.empty(... | python|numpy|loops|parallel-processing|numba | 1 |
351,607 | 70,474,556 | Unix ms timestamp pandas index (plotting polygon.io data with mplfinance) | <p>I am pulling data from <a href="https://polygon.io/docs/stocks/get_v2_aggs_ticker__stocksTicker__range__multiplier___timespan___from___to" rel="nofollow noreferrer">polygon.io</a> and it returns time as a Unix Msec timestamp as below, afterwhich I am having trouble converting this to a index that is useable by mplfi... | <p>Try replacing</p>
<pre class="lang-py prettyprint-override"><code>df.index = [from_unixtime(ts) for ts in df['t']]
</code></pre>
<p>with</p>
<pre class="lang-py prettyprint-override"><code>df.index = pd.DatetimeIndex( pd.to_datetime(df['t'],unit='s') )
</code></pre>
<p>lmk</p> | python|pandas|dataframe|mplfinance | 1 |
351,608 | 70,693,764 | OpenAIGPTModel PyTorch Error- ValueError: too many values to unpack (expected 2) | <p>I am having issues getting the Persona-Dialogue-Generation model（from github） to run. It seems that it should have only one output parameter but I provided two.How to modify the code?</p>
<p>The problem code is</p>
<p><code>self.transformer_module = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt',num_special_toke... | <p>It looks like <code>OpenAIGPTLMHeadModel</code> will only return <code>lm_logits</code>, so just remove <code>hidden_states</code> from model outputs. <code>lm_logits = self.transformer_module(input_seq, None, dis_seq)</code></p>
<pre><code> def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_... | python|pytorch | 0 |
351,609 | 70,552,801 | How to ignore NaNs when reassigning values to variables | <p>I imported and concatenated a couple of csv files. All of them contain the variable "prac_type" but the observations are listed in different ways. Some are strings (yes, no, unsure) while the others are numeric (1,2,3). Here is a look at the variable:</p>
<pre><code>print(df.prac_type.unique())
[nan 1.0 2... | <p>Try <code>df.prac_type = [prac_dic.get(item) for item in df.prac_type]</code></p> | python|pandas | 1 |
351,610 | 70,564,428 | module 'pandas' has no attribute 'sparsedtype' - Catboost classifier | <p>I am working on catboost classifier algorithm and while doing model fit - I am getting this error -module 'pandas' has no attribute 'sparsedtype'. Any suggestion pls.</p>
<p>I am using Pandas version - 0.23.4</p> | <p>SparseDtype was introduced in version 0.24.0 of pandas.</p>
<p><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.SparseDtype.html" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.SparseDtype.html</a></p>
<p>Install more recent version.</p> | pandas|catboost | 0 |
351,611 | 70,449,518 | Why am I getting Empty Dataframe message when using concat | <p>I am trying to view the predicted price for MSFT and I am referring to a book titled 'Machine learning and data science blueprints for finance'. It provides a sample code as a case study to determine the future stock price of MSFT. The code is below. However, when I start debugging it, the terminal shows the followi... | <p>This is because you drop all row containing <code>NaN</code>. You should replace</p>
<pre><code>dataset = pd.concat([Y, X], axis=1).dropna().iloc[::return_period, :]
</code></pre>
<p>with</p>
<pre><code>dataset = pd.concat([Y, X], axis=1).iloc[::return_period, :]
</code></pre>
<p>which returns:</p>
<pre><code> ... | python|python-3.x|pandas|numpy|concatenation | 1 |
351,612 | 70,535,511 | Keep entry with least missing values for a given observation in dataframe | <p>I have a dataframe that includes US company identifiers (Instrument) as well as the company's Name, ISIN and it's CIK number.</p>
<p>Here is an example of my dataset:</p>
<pre><code>dict = { "Instrument": ["4295914485", "4295913199", "4295904693", "5039191995", "... | <p>You could use the number of null values in a row as a sort key, and keep the first (lowest) of each <code>Instrument</code></p>
<pre><code>import pandas as pd
import numpy as np
dict = { "Instrument": ["4295914485", "4295913199", "4295904693", "5039191995", "503... | python|pandas|dataframe | 1 |
351,613 | 70,436,611 | Fast way to check if a list of points is close to a list of linestrings in python with shapely and geopandas | <p>I have a large list of shapely points (around 150k) and a large list of shapely linestrings (around 240k). I was wondering if there was a fast way to check if these points are close to any of the linestrings.</p>
<p>This was the code I used for 300 points and it took 387 seconds.</p>
<pre class="lang-py prettyprint-... | <ul>
<li>an approach that works well is <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.sjoin_nearest.html" rel="nofollow noreferrer">https://geopandas.org/en/stable/docs/reference/api/geopandas.sjoin_nearest.html</a></li>
<li>using UK motorway network for <em>LineString</em> and NHS hospitals for... | python|geometry|gis|geopandas|shapely | 0 |
351,614 | 70,541,324 | Is there anyway to load a .tflite without keras or tensorflow in python android app? | <p>I'm fairly new to this so please excuse mylack of knowledge. I'm trying to make an ML app with kivy, which detects certain objects. The problem is that I cannot include tensorflow and keras in my code because kivy doesn't allow apk conversion with it. So I came across tensorflow lite, which can run on android, but w... | <p>Sure. The easiest way is using TensorFlow Lite <a href="https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/package-summary" rel="nofollow noreferrer">Java API</a>, and it does not depend on TensorFlow or Keras at all.</p>
<p>You can also read the TensorFlow Lite Android quick start <a href="https://ww... | android|tensorflow|model|tensorflow-lite | 0 |
351,615 | 70,474,303 | xml elements in elements to python dataframe | <p>i'm trying to convert the xml data into pandas dataframe.
what i'm struggling is that i cannot get the elements in the element.</p>
<p>here is the example of my xml file.</p>
<p>i'm trying to extract the information of</p>
<p>-orth :"decrease"</p>
<p>-cre_date:2013/12/07</p>
<p>-morph_grp -> var type :&... | <p>Making some assumptions about what you want, here is an approach using XPath.</p>
<p>I'm assuming you will be iterating over multiple XML files that each have one superEntry root node in order to generate a DataFrame with more than one record.
Or, perhaps your actual XML doc has a higher-level root/parent element ab... | python|pandas|xml | 0 |
351,616 | 70,562,477 | How can I speed up raw data computation | <p>I have the temperature according to x,y,z as raw data.</p>
<p>I want to turn the raw data of x, y, z, t into grid data.</p>
<p>When transforming data by applying t values to the x and z coordinates according to z, I created a zero array and converted it into a pandas data frame.</p>
<p>Afterwards, the data was proce... | <p>OK, so first I would create the temporary dataset with given <code>z</code></p>
<pre><code>t_data = data[data['z']==0.25]
</code></pre>
<p>Then, I would create a new, transformed coordinates (if you would like to have resolution 1, 0.1, 0.01 it's quite easy):</p>
<pre><code>t_data['x_new'] = t_data['x'].round(0)
t_d... | python|pandas|heatmap | 0 |
351,617 | 70,643,096 | How to plot plotBox and a line plot with different axes | <p>I have a dataset that can be crafted in this way:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
date_range = pd.date_range(start='2021-11-20', end='2022-01-09').to_list()
df_left = pd.DataFrame(columns=['Date','Values'])
for d in date_rang... | <p>One of the problems is that <code>resample('W', on='Date')</code> and <code>.dt.strftime("%Y-%U")</code> seem to lead to different numbers in both dataframes. Another problem is that <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html" rel="nofollow noreferrer">boxplot</a> int... | pandas|dataframe|matplotlib|plot|boxplot | 2 |
351,618 | 70,654,893 | geopandas midpoint on line | <p>I have a geopandas dataframe of linestrings. Each line string is a single line.</p>
<p>I want to get the midpoint of the line and append the point geometry to geodataframe in a column <code>centroid</code>.</p>
<p>How do I achieve this?</p> | <ul>
<li><strong>LineString</strong> has a centroid, hence case of using it</li>
<li>solution demonstrates this with output as visual and as data</li>
</ul>
<pre><code>import geopandas as gpd
import shapely.geometry
import numpy as np
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
# pic... | python|geopandas | 2 |
351,619 | 70,736,405 | LSTM model with parameters | <p>I'm trying to make a LSTM model in Keras with the following parameters:</p>
<p><a href="https://i.stack.imgur.com/rpLo7.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/rpLo7.png" alt="enter image description here" /></a></p>
<p>However, I'm not sure my code is right.</p>
<pre><code>model = Sequent... | <p>As per the your requirement below code can be used</p>
<pre><code>model = Sequential()
model.add(LSTM(16, return_sequences=True, activation='relu', input_shape=(3, 1))) #16 neurons (?)
model.add(LSTM(32, return_sequences=True))
model.add(LSTM(64))
model.add(Dense(units =128))
model.compile(optimizer=tf.keras.op... | python|tensorflow|keras|lstm | 0 |
351,620 | 70,737,939 | The column label 'call_id' is not unique. For a multi-index, the label must be a tuple with elements corresponding to each level | <p>I have two PANDAS data-frames and I need to merge them on call_id. I have done this with different data frames. However, this time when I try</p>
<pre><code>df = pd.merge(labels, sequences, on = "call_id")
</code></pre>
<p>I get</p>
<pre><code>The column label 'call_id' is not unique.
For a multi-index, th... | <p>You have to call the merge function different:</p>
<pre><code>labels.merge(sequences, how='inner', on='call_id')
</code></pre>
<p>Please look in the <code>how=</code> method here: <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer">https://pandas.pydata.org/do... | python|pandas | 0 |
351,621 | 70,669,191 | How to add new column in dataframe based on the other dataframe? | <p>Hi I have 2 dataframes but both are not same. I have to update one based on the 2nd.</p>
<p>Example:
df1:</p>
<pre><code>Region Sub_Region Run_Date Status Reason
ASPAC CRM 2022-01-11 Success
ASPAC Genesys 2022-01-11 Failed
LATAM CRM 2022-01-11 Success
</code></pre>
<p>df2:</p>
<pr... | <p>Filter out your dataframe after <code>merge</code>:</p>
<pre><code>df1['Max_Load_Date'] = df1.merge(df2, on=['Region', 'Sub_Region'], how='left') \
.query("Status == 'Success'")['Max_Load_Date']
print(df)
# Output
Region Sub_Region Run_Date Status Reason Max_Load_Date
0 ... | python|python-3.x|pandas|dataframe | 0 |
351,622 | 70,549,144 | plot and draw curves in python matplotlib without ignoring first and last Nan values from the graph figure | <p>I have csv format file like the below table</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: left;">depth</th>
<th style="text-align: center;">x1</th>
<th style="text-align: right;">x2</th>
<th style="text-align: right;">x3</th>
</tr>
</thead>
<tbody>
<tr>
<td style="te... | <p>You need to <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.sharey.html" rel="nofollow noreferrer">share the y axis with the other y axis</a>:</p>
<pre><code>fig, axs = plt.subplots(1, 3, figsize=(15, 12), dpi=100, tight_layout=True, gridspec_kw={'wspace': 0})
axs[0].plot(df.x1, df.depth, '-o... | python|pandas|matplotlib | 1 |
351,623 | 70,695,645 | How to display Rows based on number of Days (From Dates) in Python Pandas | <p>I have a Dataframe which looks like below after some coding. Now, I want to display only those rows where "Name" is repeating 3 or more days.</p>
<p>Sample Data Frame:</p>
<p><a href="https://i.stack.imgur.com/q4u2D.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/q4u2D.png" alt="Sample D... | <p>Welcome u2k to Stackoverflow.</p>
<p>You could try:</p>
<pre><code>name_count = df.Name.value_counts()
df[df.Name.isin(name_count.index[name_count.gt(2)])]
</code></pre>
<hr />
<p><strong>Output:</strong></p>
<pre><code> Name Account
0 Ryan 1
1 Ryan 2
2 Ryan 3
3 Ryan 4
</code></pre>
<hr />
... | python|pandas|dataframe|datetime|timedelta | 0 |
351,624 | 70,629,095 | How do I store different arrays from an array in a list? | <p>I have a problem where I cant store different arrays in a list. I've got an array <code>displacement = [[0],[0],[0],[1],[5.5],[-7],[0],[0],[0]]</code> and I want to break it into <em>n</em> arrays with six rows and one column and then store the different arrays in a list.</p>
<p>I tried this:</p>
<pre><code> elem... | <p>It seems like you want to partition 'displacement' into 'element_count' elements of 'num_columns' (6 in your case) columns. Such that you populate each element in order from your 'displacement' array. (I assumed that if you want more elements with more columns than 'displacement' allows, you just butt up against the... | python|arrays|list|numpy|append | 0 |
351,625 | 70,570,790 | How to filter pandas dataframe to select list of column | <p>I have a dataframe with 100 columns and I want to select list of variables</p>
<pre><code>ID A B C
0 day1 day10 Δday day1 day10 Δday day1 day10 Δday
1 1 1.0 2.0 1.0 1.5 2.5 1.0 3.0 2.0 -1.0
2 2 3.0 5.0 2.0 1.0 2.5 1.5 ... | <p>Try <code>slice(None)</code> to select any column at first level:</p>
<pre><code>>>> df.loc[:, (slice(None), 'Δday')]
A B C
Δday Δday Δday
0 1.0 1.0 -1.0
1 2.0 1.5 2.0
2 1.0 1.4 0.5
</code></pre>
<p>To know more: <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced... | python|pandas|dataframe|pandas-groupby | 1 |
351,626 | 70,707,996 | matplotlib: horizontal labels as style | <p>Is there a parameter to force horizontal labels <strong>in an mplstyle file? and/or using rcParams</strong>?</p>
<p>I'm currently using <code>ax.xaxis.set_tick_params(rotation=0)</code> at plot construction. I'd like a permanent style or setting. Thanks!</p>
<p>Default look (with <code>x_compat=True</code> in a pand... | <p>Use parameter <code>rot</code> from <code>df.plot</code></p>
<pre><code>df.plot(ax=ax, x='Date', x_compat=True, rot=0)
</code></pre> | pandas|matplotlib | 1 |
351,627 | 70,534,732 | datetime index in plotting doesn't work. why? | <p>I want to plot one month of data [one data per hour. the dataset is over one year]. why is this code not working?</p>
<pre><code>df_pv["Zeit"] = pd.to_datetime(df_pv["Zeit"])
df_pv["Zeit"].min(), df_pv["Zeit"].max()
[Out] (Timestamp('2020-01-01 01:00:00'), Timestamp('2020-12-3... | <p>convert Zeit to datatime using pd.To_DateTime()</p>
<pre><code>df_pv=pd.DataFrame()
df_pv["Zeit"] = pd.date_range(pd.Timestamp('2020-01-01 01:00:00'), pd.Timestamp('2020-12-31 00:00:00'))
df_pv.set_index("Zeit", inplace=True)
df_pv['y']=np.random.randn(len(df_pv))
df_pv.plot()
</code></pre> | python|pandas|datetime|timestamp | 0 |
351,628 | 70,599,172 | Turn group of points into single pixel | <p>I have a b&w image with 4 groups of points, and I need to transform each group into a single point.</p>
<p>My idea was to find remove points by distance, so if a pixel is maybe 20px away from the other, I would turn remove one and keep the other, and repeat the process until I only got 4 pixels, but it doesn't s... | <p>Your idea is not so far from a workable solution. You can craft a simple algorithm that works as follows:</p>
<ul>
<li><p>scan the image in raster order until you meet a white pixel,</p>
</li>
<li><p>from this pixel, erase the whole connected component by seed-filling,</p>
</li>
<li><p>restore the initial pixel and ... | python|numpy|opencv|image-processing|computer-vision | 2 |
351,629 | 43,007,467 | Setting the shape of a tensor as the shape of another tensor | <p>I'm trying to run this piece of code:</p>
<pre><code>def somefunc(x, rows, n_hidden):
vectors = tf.contrib.layers.embed_sequence(nodes, vocab_size=vocab_size, embed_dim=n_hidden)
batch_size = tf.shape(vectors)[0]
state = tf.zeros([batch_size, rows, n_hidden])
bias = tf.Variable(tf.constant(0.1, shap... | <p>The <code>shape</code> argument of the <a href="https://www.tensorflow.org/api_docs/python/tf/constant" rel="nofollow noreferrer"><code>tf.constant()</code></a> op expects a <strong>static</strong> shape, so you can't use a <code>tf.Tensor</code> as part of the argument.</p>
<p>Fortunately there is another op that ... | tensorflow | 2 |
351,630 | 43,008,903 | Does a column have only dates or also datetimes in pandas? | <p>I'm trying to write a function to assess whether a column in a pandas DataFrame has only dates or also datetimes, in order to decide whether to create a date or timestamp column in an external database.</p>
<p>All dates in a DataFrame are stored as the same type, but date-only values would have no time component</p... | <p>I'd check to see if the <code>datetime</code> column is equal to its <code>date</code> component`</p>
<p>Consider the dataframe <code>df</code></p>
<pre><code>df = pd.DataFrame(dict(
Date=pd.to_datetime(
['2017-03-01',
'2017-03-01 00:00:00',
'2017-03-01 00:00:01',
... | pandas|numpy | 1 |
351,631 | 42,973,621 | How to keep track of the label in supervised learning? | <p>I have 144 images named "Good_id" and "Bad_id".</p>
<p>Now I have read all the images and extracted 13 features from them and stored in the numpy array of shape (144,13).</p>
<p>What I don't understand is how do I tell the classifier( I am going to use svm for this ) that images in the array are from classes Good ... | <p>Save the classes into a separate array <code>y</code>, encoding "Good_id" as 1 and "Bad_id" as 0 (in the same order as they appear in your (144,13) array). Then when you use SVM you pass both numpy arrays, in the following way:</p>
<pre><code>>>> import numpy as np
>>> X = np.array([[-1, -1], [-2,... | python|numpy|machine-learning|svm | 2 |
351,632 | 42,743,167 | how to fetch a value from a dataFrame cell to fill in value at another location? | <p>I have the following DataFrame:</p>
<pre><code>ID | Parent ID | Direction
1 | 0 | North
2 | 1 | South
3 | 1 | West
4 | 0 | East
</code></pre>
<p>I want to write a function that will change the direction for all rows that have a non-zero 'Parent ID' to the Direction of the corre... | <p>You can try this:</p>
<pre><code>to_replace = df['Parent ID'] != 0
df.loc[to_replace, 'Direction'] = df['Parent ID'][to_replace].map(df.set_index("ID").Direction)
df
</code></pre>
<p><a href="https://i.stack.imgur.com/GMTYB.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/GMTYB.png" alt="ente... | python|pandas | 1 |
351,633 | 42,865,972 | dropping rows where date aligns with max value of another column in pandas | <p>I have a <code>DataFrame</code> named <code>au</code> from which I want to drop the rows where <code>date</code> aligns with observations where <code>bal==bal.max()</code>. For example, if <code>bal==bal.max()</code> is associated with <code>2009-08-01</code>, then I want to drop all other observations for which <co... | <p>Using <code>idxmax</code> and @jezrael's setup</p>
<p><strong><em>setup</em></strong></p>
<pre><code>au = pd.DataFrame({'bal':[1,2,3,4],
'date':['2009-08-01','2009-08-01','2009-08-02', '2009-08-02'],
'C':[7,8,9,1]})
</code></pre>
<p><strong><em>solution</em></strong> </p>
<... | python|pandas | 3 |
351,634 | 42,708,031 | Filtering a numpy array using another array of labels | <p>Given two numpy arrays, i.e:</p>
<pre><code>images.shape: (60000, 784) # An array containing 60000 images
labels.shape: (60000, 10) # An array of labels for each image
</code></pre>
<p>Each row of <code>labels</code> contains a <code>1</code> at a particular index to indicate the class of the related example in <... | <p>One way would be to convert your label array to bool and use it for indexing:</p>
<pre><code>classes = []
blabels = labels.astype(bool)
for i in range(10):
classes.append(images[blabels[:, i], :])
</code></pre>
<p>Or as a one-liner using list comprehension:</p>
<pre><code>classes = [images[l.astype(bool), :] ... | python|arrays|numpy|filter | 1 |
351,635 | 42,594,070 | create dataframe from dataset loop func | <p>Hey I have problem creating dataframe from the boston dataset (can be found here: <a href="https://archive.ics.uci.edu/ml/datasets/Housing" rel="nofollow noreferrer">https://archive.ics.uci.edu/ml/datasets/Housing</a>)</p>
<p>So this is my code:</p>
<pre><code>data1 = DataFrame(data= np.c_[boston['data'], boston['... | <p>I think you need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_fwf.html" rel="nofollow noreferrer"><code>read_fwf</code></a>:</p>
<pre><code>cols = ['col1','col2','col3','col4','col5','col6','col7',
'col8','col9','col10','col11','col12','col13','col14']
url = 'https://archive.ic... | python|pandas|dataframe | 0 |
351,636 | 42,791,793 | Pandas: convert series of time stamps keeping the YYYY-MM-DD format only | <p>Say you want to create a series of time stamps in the form <code>YYYY-MM-DD</code> from <code>1990-1-1</code> to <code>1991-12-31</code> with <code>pandas</code>:</p>
<pre><code>import pandas
import datetime
start = datetime.date(1990, 1, 1)
end = datetime.date(1991, 12, 31)
s = pandas.Series(pandas.date_range(sta... | <p>use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.strftime.html" rel="nofollow noreferrer"><code>.dt.strftime</code></a> to output your dates in your desired string format:</p>
<pre><code>In [114]:
s.dt.strftime('%Y-%m-%d').head()
Out[114]:
0 1990-01-01
1 1990-01-02
2 199... | python|date|pandas|datetime | 3 |
351,637 | 42,735,183 | Pandas: using dict (including operators) to return column subset from dataframe | <p>Let's say I got a data frame with columns <code>a, b, c, d, e</code> and a dictionary <code>{"A": "a", "B": "b", "E": "e"}</code></p>
<p>a) How do I use this dictionary to return a new data frame (same index) with only those 3 columns (renamed to cap letters)?</p>
<p>b) Alternatively, is there a way to drop any co... | <p>You could use <code>eval</code>-- not the Python function of the same name, but the <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.eval.html#pandas.DataFrame.eval" rel="nofollow noreferrer">DataFrame method</a>.</p>
<pre><code>In [50]: df = pd.DataFrame(np.arange(15).reshape((3,5)),... | python|pandas|calculated-columns | 4 |
351,638 | 42,628,577 | Fastest way to cast all dataframe columns to float - pandas astype slow | <p>Is there a faster way to cast all columns of a pandas dataframe to a single type? This seems particularly slow:</p>
<pre><code>df = df.apply(lambda x: x.astype(np.float64), axis=1)
</code></pre>
<p>I suspect there's not much I can do about it because of the memory allocation overhead of <code>numpy.ndarray.astype<... | <p>No need for <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html" rel="noreferrer"><code>apply</code></a>, just use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.astype.html" rel="noreferrer"><code>DataFrame.astype</code></a> directly. </p>
<pr... | python|performance|pandas|numpy|dataframe | 21 |
351,639 | 42,687,637 | How to create and empty scatter plot with date on the x axis - Python, Pandas? | <p>I have a dataframe that looks like this:</p>
<pre><code>date number_of_books ... (additional columns)
1997/06/01 23:15 3
1999/02/19 14:56 5
1999/10/22 18:20 7
2001/11/04 19:13 19
... ...
2014/04/30 02:14 134
</code></pre>
<p>My goal is to c... | <p>You could create a column in your <code>pd.DataFrame</code> to store the color information, and pass the arguments to each data point with the <code>scatter</code> plot function.</p>
<p>See for example:</p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
# your dataframe
df = pd.DataFrame({"date": ... | python-3.x|pandas|datetime|matplotlib|scatter | 0 |
351,640 | 42,950,161 | Python Numpy calculation without looping | <p>So I need to transform the following line </p>
<pre><code>np.mean([-y[i] * X[i] * (1 - 1 / (1 + np.exp( - np.dot(X[i],w) * y[i]))) for i in range(X.shape[0])], axis = 0)
</code></pre>
<p>where <code>y.shape = (N,)</code>, <code>X.shape = (N,M)</code>, <code>w.shape = (M,)</code>
and the output's shape has to be ... | <p>Here's one vectorized approach making use of the efficient <code>matrix-multiplication</code> with <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html" rel="nofollow noreferrer"><code>np.dot</code></a> -</p>
<pre><code>n = y.shape[0]
exp_val = (1 - 1 / (1 + np.exp(-np.dot(X,w)*y)))
out = -(... | python|performance|numpy|vectorization | 1 |
351,641 | 42,615,702 | What's a more efficient way to merge rows from DataFrames row-by-row with conditions? | <p>I'm joining two tables with data from two systems. A simple Pandas <strong>merge</strong> between two df won't honor more complex rules (unless I'm using it wrong, don't understand the process merge is implementing--very possible).</p>
<p>I've cobbled together a <strong>toy solution</strong> that lets me unpack two... | <p>df3=df1.join(df2) does not do what you want?</p> | pandas|join|dataframe | 0 |
351,642 | 42,693,387 | Read url as pandas dataframe with column names (python3) | <p>I have read several questions regarding this topic, but nothing seems to work for me. </p>
<p>I want to retrieve the data from this page "<a href="http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/heart/heart.dat" rel="nofollow noreferrer">http://archive.ics.uci.edu/ml/machine-learning-databases/stat... | <p>The link you provided was missing a hyphen. I've corrected that in my answer. Basically you need to decode the <code>s</code> string into <code>utf-8</code>, then split it on <code>\n</code> to get each row and then split each row on white space to get each value separately. This will give you a nested list represen... | python|pandas|url | 1 |
351,643 | 42,703,575 | How do I separate a time stamp into separate columns with pandas? | <p>I would like to separate my timestamp into different columns, as I would like to sort my data by year. My time stamp looks like this: </p>
<pre><code> 12/08/2011 11:04:13 AM
</code></pre>
<p>Sample of data:</p>
<pre><code>BusinessName DBAName LegalOwner NameLast NameFirst ISSDTTM
NEW GARDEN RESTAURA... | <p>For starters, the format you are passing to strptime, '%Y %m %d %H' doesn't match the date/time string you gave as an example. Also, your datetime is only in one column, why are you passing [1,2,3,4] in the parse_dates argument?</p>
<p>Assuming the contents of your CSV are in a file called foo.csv (no header) and c... | python|pandas | 0 |
351,644 | 42,970,106 | ModuleNotFoundError: No module named 'tensorflow' | <p>When I'm importing TensorFlow with Spyder as so:</p>
<pre><code>import tensorflow as tf
</code></pre>
<p>I then face the following error:</p>
<blockquote>
<p><strong>ModuleNotFoundError: No module named 'tensorflow'</strong></p>
</blockquote>
<p>How can I overcome this issue?</p> | <p>Since you plan to use tensforflow with Anaconda, you need to install it through Anaconda. It is advisable to do this as follows: </p>
<ol>
<li><code>C:> conda create -n tensorflow</code></li>
<li><code>C:> activate tensorflow</code></li>
<li><code>C:> pip install --ignore-installed --upgrade https://stor... | python|tensorflow|jupyter|spyder | 7 |
351,645 | 42,980,205 | pandas agg for datetime returns incorrect result | <p>groupby.agg() return incorrect result(or at lease very misleading) for datatime series. Here is the code snippet (Pandas version : 0.19): </p>
<pre><code>In [4]: df = pd.DataFrame({'A':[1,2,3,4],'B':pd.to_datetime("2017-01-01")})
In [5]: df
Out[5]:
A B
0 1 2017-01-01
1 2 2017-01-01
2 3 2017-01-01... | <pre><code>df.groupby("A")['B'].agg('nunique')
</code></pre> | python|pandas | 1 |
351,646 | 42,951,315 | Represent negative timedelta in most basic form | <p>If I create a negative <code>Timedelta</code> for e.g. 0.5 hours, the internal representation looks as follow:</p>
<pre><code>In [2]: pd.Timedelta('-0.5h')
Out[2]: Timedelta('-1 days +23:30:00')
</code></pre>
<p>How can I get back a (<code>str</code>) representation of this <code>Timedelta</code> in the form <code... | <p>I can't add comment to you so adding it here. Don't know if this helps but I think you can use python humanize.</p>
<pre><code>import humanize as hm
hm.naturaltime((pd.Timedelta('-0.5h')))
</code></pre>
<p>Out:</p>
<pre><code>'30 minutes from now'
</code></pre> | pandas|timedelta|python-datetime | 1 |
351,647 | 42,804,573 | TypeError: cannot do slice indexing on <class 'pandas.indexes.numeric.Int64Index'> with these indexers [(2,)] of <class 'tuple'> | <p>I've a user defined function as follows:-</p>
<pre><code>def genre(option,option_type,*limit):
option_based = rank_data.loc[rank_data[option] == option_type]
top_option_based = option_based[:limit]
print(top_option_based)
top_option_based.to_csv('top_option_based.csv')
return(top_option_based))... | <p>Consider the dataframe <code>rank_data</code></p>
<pre><code>rank_data = pd.DataFrame(dict(
genre=['Crime'] * 4 + ['Romance'] * 4
))
print(rank_data)
genre
0 Crime
1 Crime
2 Crime
3 Crime
4 Romance
5 Romance
6 Romance
7 Romance
</code></pre>
<p>I'm going to assume you wanted to g... | python|python-3.x|pandas|slice|argument-unpacking | 1 |
351,648 | 42,890,970 | Pythonic way to convert a numpy array into another array with column indices | <p>So, what I want to do, given this input:</p>
<pre><code>a=np.array([[5, 1, 10], [2, 3, 4]])
</code></pre>
<p>convert into another np array:</p>
<pre><code>[[(5, 0), (1, 1), (10, 2)], [(2, 0), (3, 1), (4, 2)]]
</code></pre>
<p>What's the pythonic way to do this?</p>
<p>EDIT: I was using 1 indexing but 0 indexing... | <p>First your <code>a</code> expression is missing []</p>
<pre><code>In [231]: a=np.array([[5, 1, 10], [2, 3, 4]]) # add extra []
In [232]: a
Out[232]:
array([[ 5, 1, 10],
[ 2, 3, 4]])
</code></pre>
<p>A list comprehension is the easiest way to produce the shown list</p>
<pre><code>In [233]: [[(n,i+1) fo... | python|numpy | 4 |
351,649 | 42,740,483 | Convert array of integers into dictionary of indices | <p>I have a (large) integer array like</p>
<pre><code>materials = [0, 0, 47, 0, 2, 2, 47] # ...
</code></pre>
<p>with few unique entries and I'd like to convert it into a dictionary of indices, i.e.,</p>
<pre><code>d = {
0: [0, 1, 3],
2: [4, 5],
47: [2, 6],
}
</code></pre>
<p>What's the most ef... | <p>no need for <code>numpy</code>, those are standard python structures, dict comprehension does that very well for your problem:</p>
<pre><code>materials = [0, 0, 47, 0, 2, 2, 47]
d = {v : [i for i,x in enumerate(materials) if x==v] for v in set(materials)}
print(d)
</code></pre>
<p>result:</p>
<pre><code>{0: [0,... | python|arrays|numpy | 4 |
351,650 | 27,231,783 | How to groupby an index as well as a column in pandas | <p>Let's say I have a simple dataframe which has an index of a datetime and three columns - one being a value and one being an aggregating indicator and the last being a unique identifier. The index is not unique across the dataframe, as it is duplicated over multiple instances of the indicator. ie it looks like this:<... | <p>You can do it like this:</p>
<pre><code>>>> df.reset_index().groupby(['index','aggregating indicator'])['val'].sum().reset_index()
index aggregating indicator val
0 1-Jan set_a 4
1 1-Jan set_b 5
2 2-Jan set_a 6
3 2-Jan set_b ... | python|pandas|aggregate | 2 |
351,651 | 27,174,771 | Convert an numpy array with header to float | <p>I wanted to use the function <code>.astype(float)</code> to convert an array named Defocus_Array to float. But I got this error.</p>
<pre><code>>>> Defocus_Array.astype(float)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: could not convert string to float:... | <pre><code>Defocus_Array = np.array(('123.3', '0', 'defocus'))
Defocus_Array.astype(float) # ValueError: could not convert string to float: defocus
Defocus_Array[2] = 'nan'
Defocus_Array.astype(float) # array([ 123.3, 0. , nan])
</code></pre>
<p>Or, more generally:</p>
<pre><code>Defocus_Array[Defocus_Array ... | python|arrays|numpy | 1 |
351,652 | 27,191,310 | install numpy and matplotlib of python2.7 numpy on window7 64 system | <p>I installed python 2.7 in the window 7 64-bit system, it works.However, when I install the numpy and matplotlib , the setup can not find the path,
the version of matplotlib is 'matplotlib-1.4.2.win-amd64-py2.7.exe'
it showed the error'Python version 2.7 required, which was not found in the regestry'.</p>
<p>I have ... | <p>Alles, </p>
<p>I think I did resolved the problems! </p>
<p>The most tricky issue is </p>
<p>when installing some Python Windows modules lies in: you have 64-bit Python, but a 32-bit installer of new modules.</p>
<p>64-bit Python installer write to: HKLM|HKCU\SOFTWARE\</p>
<p>while 32-bit installer looks at : H... | python|python-2.7|numpy|matplotlib | 0 |
351,653 | 27,241,253 | print the unique values in every column in a pandas dataframe | <p>I have a dataframe (df) and want to print the unique values from each column in the dataframe.</p>
<p>I need to substitute the variable (i) [column name] into the print statement</p>
<pre><code>column_list = df.columns.values.tolist()
for column_name in column_list:
print(df."[column_name]".unique()
</code></p... | <p>It can be written more concisely like this:</p>
<pre><code>for col in df:
print(df[col].unique())
</code></pre>
<p>Generally, you can access a column of the DataFrame through <a href="http://pandas.pydata.org/pandas-docs/stable/indexing.html#basics" rel="noreferrer">indexing</a> using the <code>[]</code> opera... | python|for-loop|pandas | 108 |
351,654 | 27,004,908 | What actually happens when broadcasting a NumPy array | <p>I was playing around with NumPy and I've written simple function</p>
<pre><code>> def euclid_dist(x, y):
... return sqrt((x-y).transpose().dot(x-y))
</code></pre>
<p>But now when I try</p>
<pre><code>> x = arange(1,4).reshape(3,1)
> y = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]]).transpose()
> z ... | <p>As I understand your question, you are trying to do a column-wise parallel dot product. In other words, for a matrix of vectors <code>a</code>, <code>b</code>, <code>c</code>, and <code>d</code>, you want to take this:</p>
<pre><code>[[a1, b1, c1, d1],
[a2, b2, c2, d2],
[a3, b3, c3, d3]]
</code></pre>
<p>To this... | python|numpy | 2 |
351,655 | 14,549,696 | Mipmap of image in numpy? | <p>I'm checking with you if there is a neat numpy solution to resizing down a 2D numpy array (which is an image) using bilinear filtering?</p>
<p>More specifically, my array has the shape (width, height, 4) (as in a rgba image). The downscaling is also only done on "even" steps: i.e. from (w, h, 4) to (w/2, h/2, 4) to... | <p>I don't think there is any specific solution in numpy, but you should be able to implement it efficiently without leaving the comfort of python. Correct me if I'm wrong, but when the size of the image is divisible by 2, a bilinear filter is basically the same as averaging 4 pixels of the original image to get 1 pixe... | python|numpy | 3 |
351,656 | 14,916,407 | How do I stack vectors of different lengths in NumPy? | <p>How do I stack column-wise <code>n</code> vectors of shape <code>(x,)</code> where x could be any number?</p>
<p>For example, </p>
<pre><code>from numpy import *
a = ones((3,))
b = ones((2,))
c = vstack((a,b)) # <-- gives an error
c = vstack((a[:,newaxis],b[:,newaxis])) #<-- also gives an error
</code></pre... | <p>Short answer: you can't. NumPy does not support jagged arrays natively.</p>
<p>Long answer:</p>
<pre><code>>>> a = ones((3,))
>>> b = ones((2,))
>>> c = array([a, b])
>>> c
array([[ 1. 1. 1.], [ 1. 1.]], dtype=object)
</code></pre>
<p>gives an array that <em>may or may not</... | python|numpy | 41 |
351,657 | 25,196,595 | Memory optimization when selecting from a pandas dataframe | <p>I have a rather large pandas dataframe (1.7G) from which I am selecting some columns to do some computaton (find maximum value of the three selected columns). It seems that this operation is memory intensive. I am trying to find a way to avoid this memory overhead.</p>
<p>For the purpose to this question, I a simpl... | <p>Pandas returns copies for most operations. Certain selection operations can return a <em>view</em>, in that the memory may not be copied and is an underlying numpy view. This is in general controlled by numpy. A taking operation like you are doing, (e.g. a non-consecutive) slice, will never give a view.</p>
<p>Howe... | python|pandas | 1 |
351,658 | 25,273,415 | How to plot a PMF of a sample? | <p>Is there any function or library that would help me to plot a probability mass function of a sample the same way there is for plotting the probability density function of a sample ?</p>
<p>For instance, using pandas, plotting a PDF is as simple as calling:</p>
<pre><code>sample.plot(kind="density")
</code></pre>
... | <p>If <code>ts</code> is a series, you may obtain PMF of the sample by:</p>
<pre><code>>>> pmf = ts.value_counts().sort_index() / len(ts)
</code></pre>
<p>and plot it by:</p>
<pre><code>>>> pmf.plot(kind='bar')
</code></pre>
<hr>
<p>numpy only solution can be done using <a href="http://docs.scipy... | python|matplotlib|plot|pandas|scipy | 15 |
351,659 | 25,065,697 | Store to HDF, can't store frequency | <p>I have dataframe that has a custom frequency index, like so,</p>
<pre><code>holidays = CustomBusinessDay(holidays=[ pnd.Timestamp(d) for d in pnd.Series.from_csv(f).values])
timestamps = pnd.date_range(s, e, normalize=False, freq = holidays)
df = pnd.DataFrame(columns= ['a','b'], index= timestamps)
</code></pre>
<... | <p>Jeff's answer is correct. I was able to use format='table' and not get the error "can't set attribute 'freq' in node"</p> | python|pandas | 0 |
351,660 | 25,274,270 | How can I convert each Pandas Data Frame row into an object including the column values as the attributes? | <p>Suppose I have a DataFrame including following columns "NAME", "SURNAME", "AGE" and I would like to create one object for each row, including those column values as its variables.</p>
<pre><code>person = ConvertRow2Object(frame.iloc[0,:])
print person.NAME //outputs Gary
</code></pre>
<... | <p>You can convert the whole thing to a numpy recarray, then each record in the array is attributed:</p>
<pre><code>people = frame.to_records()
person = people[0]
print person.NAME // ...
</code></pre>
<p>Using a namedtuple also seems to work:</p>
<pre><code>from collections import namedtuple
Person = namedtuple('P... | python|pandas | 14 |
351,661 | 25,168,058 | Python pandas module openpxyl version issue | <p>My installed version of the python(2.7) module pandas (0.14.0) will not import. The message I receive is this:</p>
<p>UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.</p>
<p>Here's the problem - I already have openpyxl version 1.8.6 installed so I can't figure out what the ... | <p>The best thing would be to remove the version of openpyxl you installed and let Pandas take care.</p> | python|pandas|openpyxl|versions | 0 |
351,662 | 25,071,554 | How to feed sqlite query data to Pandas scatter_matrix | <p>I am successfully pulling data from a Fitbit sqlite db using Python sqlite3 as follows. I want to create Pandas scatter_matrix on the data. </p>
<p>My code that successfully gets data is:</p>
<pre><code>import pandas.io.sql as psql
import sqlite3 as lite
from pandas.tools.plotting import scatter_matrix
con = lit... | <p>Looks like the pandas.io.sql read_sql has some additional parameters to get column headers. I changed the read_sql statement from</p>
<pre><code>fitbit_data_psql = psql.read_sql(sql, con)
</code></pre>
<p>to </p>
<pre><code>fitbit_data_psql = psql.read_sql(sql, con, index_col=None, coerce_float=True)
</code></pre... | python|pandas | 0 |
351,663 | 25,201,143 | numpy.ndarray vs pandas.DataFrame | <p>I need to make a strategic decision about choice of the basis for data structure holding statistical data frames in my program.</p>
<p>I store hundreds of thousands of records in one big table. Each field would be of a different type, including short strings. I'd perform multiple regression analysis and manipulati... | <p><code>pandas.DataFrame</code> is awesome, and interacts very well with much of numpy. Much of the <code>DataFrame</code> is written in Cython and is quite optimized. I suspect the ease of use and the richness of the Pandas API will greatly outweigh any potential benefit you could obtain by rolling your own interface... | python|python-3.x|numpy|pandas | 25 |
351,664 | 30,311,211 | Pandas groupby to find percent True and False | <p>I have a column of sites: ['Canada', 'USA', 'China' ....]</p>
<p>Each site occurs many times in the SITE column and next to each instance is a true or false value.</p>
<pre><code>INDEX | VALUE | SITE
0 | True | Canada
1 | False | Canada
2 | True | USA
3 | True | USA
</code></pre>
<p>And it goe... | <p>Something like this:</p>
<pre><code>In [13]: g = df.groupby('SITE')['VALUE'].mean()
In [14]: g[g > 0.1]
Out[14]:
SITE
Canada 0.5
USA 1.0
</code></pre> | python|python-2.7|pandas | 14 |
351,665 | 30,562,678 | python pandas get first available datapoint of a year / calculate YTD return | <p>I need to calculate the year-to-date relative return of a given dataset. I usually caculate the cumulative relative return with this simple function:</p>
<pre><code>def RelPerf(price):
RelPerf = (price/price[0])
return RelPerf
</code></pre>
<p>The problem ist that I need to set instead of "price[0]" the ... | <p>Use df for dataframe</p>
<p>Group the data with TimeGrouper to get things grouped by year
<code>GroupedDat = df.groupby(pd.TimeGrouper('A'))</code></p>
<p>Create a new column with YTD data of adjusted close, using a transformation lambda function applied to our group data.</p>
<p><code>df["YTD"] = GroupedDat['CLO... | python|pandas | 0 |
351,666 | 30,670,121 | Python u-law issues: unknown format: 7 | <p>I'm trying to compare two large sets of wav files to remove duplicates. The issue is that one set is PCM, the other has been u-law'd. When I try to read in PCM wav, no problem, but the u-law files give the following error:</p>
<pre><code>>>> wav = wave.open("C:\\soundfiles\\Olympus Recordings\\1019.wav")... | <p>From <a href="https://docs.python.org/3/library/wave.html" rel="nofollow">the documentation</a> (emphasis mine):</p>
<blockquote>
<p>The <code>wave</code> module provides a convenient interface to the WAV sound format. <strong>It does not support compression/decompression</strong>, but it does support mono/stereo... | python|numpy|format|fft|wav | 2 |
351,667 | 30,386,437 | How does numpy.linalg.eig decide on order in which eigenvalues are returned? | <p>When I use <code>numpy.linalg.eig</code> like</p>
<pre><code>eValues, eVectors = numpy.linalg.eig(someMatrix)
</code></pre>
<p>the eValues returned are almost in descending order.</p>
<p>How does numpy.linalg.eig decide on order in which eigenvalues are returned?</p> | <p>Numpy makes no guarantees about that -</p>
<p>from the <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eig.html" rel="nofollow">docstring</a>:</p>
<pre class="lang-html prettyprint-override"><code>Returns
-------
w : (..., M) array
The eigenvalues, each repeated according to its multi... | python|numpy|linear-algebra|eigenvalue | 3 |
351,668 | 30,507,442 | Pandas: add dataframes to dataframe - match on index and column value | <p>I am trying to add pandas dataframes to another dataframe with different lengths such that the values in the result are aligned with both the (time)index and a key value from a column that is present in all dataframes. </p>
<p>Say I want to combine df1,df2 and df3 and merge on index and column 'id':</p>
<pre><code... | <p>If your DataFrames look like this:</p>
<pre><code>import datetime as DT
import numpy as np
import pandas as pd
df1 = pd.DataFrame({'id':[1,2,1,2], 'value1':[13,14,15,16]}, index=pd.DatetimeIndex(['2015-5-1', '2015-5-1', '2015-5-2', '2015-5-2']))
df2 = pd.DataFrame({'id':[1,1], 'value2':[4,5]}, index=pd.DatetimeInd... | python|pandas|merge | 1 |
351,669 | 30,607,817 | Pandas: How do I check for value match between columns in same dataframe? | <p>I am a complete coding novice and have been experimenting with Pandas. This is my first post. Thank you in advance for your help!</p>
<p>I would like to remove any rows where cat1 does not match either dog1 or dog2. It does not have to match both, just one or the other. </p>
<pre><code> cat1 dog1 dog2
0 re... | <p>This is really simple:</p>
<pre><code>df.query('cat1 == dog1 or cat1 == dog2')
</code></pre> | python|pandas | 2 |
351,670 | 30,294,633 | Applying zscore function for every row in selected columns of Pandas data frame | <p>With the following code:</p>
<pre><code>import pandas as pd
from sklearn.preprocessing import scale
df = pd.DataFrame({"Probe":["1430378_at","1439896_at","1439896_at"],
"Gene":["2900011G08Rik","Trappc5","Limk2"],
"A.x1":[0.0767, 0.4383, 0.7866],
"A.x2":[0... | <p>With Pandas it's often a good idea to try and use <code>apply</code> together with an anonymous function to perform your calculation on every row. Does this work for you?:</p>
<pre><code> df.iloc[:,2:5] = df.filter(regex = 'A.x').apply(
lambda V: scale(V,axis=0,with_mean=True, with_std=True,copy=Fals... | python|pandas | 1 |
351,671 | 30,411,203 | incorrect mean from PANDAS dataframe | <p>So here's an interesting thing:</p>
<p>Using python 2.7:</p>
<p>I've got a dataframe of about 5,100 entries, each with a number (melting point) in a column titled 'Tm'. Using the code:</p>
<pre><code>self.sort_df[['Tm']].mean(axis=0)
</code></pre>
<p>I get a mean of:</p>
<pre><code>Tm 92.969204
dtype: float6... | <p>A more readable syntax would be : </p>
<pre><code>sort_df['Tm'].mean()
</code></pre>
<p>Try to do a <code>sort_df['Tm'].value_counts()</code> or <code>sort_df['Tm'].max()</code> to see what values are present. Some unexpected values must have crept up.</p>
<p>The <code>.mean</code> function gives accurate result ... | python|python-2.7|pandas | 1 |
351,672 | 30,455,638 | How to bin all subsets of a python list into n bins | <p>I have a list:</p>
<pre><code>a = range(2)
</code></pre>
<p>and I am trying to get the list's contents binned into n(=3) bins, in all possible ways, giving (order not important):</p>
<pre><code>[[[],[0],[1]],
[[],[1],[0]],
[[],[0,1],[]],
[[],[],[0,1]],
[[0],[1],[]],
[[0],[],[1]],
[[1],[0],[]],
[[1],[],[0]],
[[0,1... | <p>Assuming I understand your aim (not sure what you might want to happen in cases of duplicate elements, namely whether you want to consider them distinct or not), you could use <code>itertools.product</code>:</p>
<pre><code>import itertools
def everywhere(seq, width):
for locs in itertools.product(range(width),... | python|numpy|combinations|sympy|itertools | 4 |
351,673 | 30,669,391 | Custom reading CSV files (Keyword accesible / custom structure) | <p>I am trying to do the following:
I downloaded a csv file containing my banking transactions of the last 180 days.
I want to readin this csv file and then do some plots with the data.
For that I setup a program that reads the csv file und makes the data avaible through keywords.
e.g. in the csv file there is a column... | <p>Repeating your array generation without the extra code:</p>
<pre><code>In [230]: dt=np.dtype([('date', 'S15'), ('value', '<f8')])
In [231]: data=np.recarray((2,),dtype=dt)
In [232]: type(data['date'])
Out[232]: numpy.core.records.recarray
In [233]: type(data['value'])
Out[233]: numpy.ndarray
</code></pre>
<p>... | python|csv|numpy|matplotlib | 0 |
351,674 | 30,675,679 | Python returns instance instead of array | <p>The structure of my program is simple. I have two <code>.py</code> files, say <code>file1.py</code> and <code>file2.py</code>. <code>file1</code> is UI, <code>file2</code> is calculations. When a button is pressed, <code>file1</code> sends the user input to <code>file2</code> and <code>file2</code> does calculations... | <p>Since <code>Calculations</code> is a class, this line invokes the implicit constructor:</p>
<pre><code>self.myPlotData = file2.Calculations(*user info*)
</code></pre>
<p>And you should know that the result of a constructor is an instance of the class. Which is exactly why you are getting that <code>type</code> inf... | python|numpy|matplotlib|plot|instance | 0 |
351,675 | 30,384,908 | Python keras neural network (Theano) package returns an error about data dimensions | <p>I have this code:</p>
<pre><code>import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from sklearn import datasets
import theano
iris = datasets.load_iris()
X = iris.data[:,0:3] # we only take the first two features.
Y = i... | <p>You specified the wrong output dimensions for your internal layers. See for instance this example from the Keras documentation:</p>
<pre><code>model = Sequential()
model.add(Dense(20, 64, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, 64, init='uniform'))
model.add(Activa... | python|numpy|canopy|theano|keras | 7 |
351,676 | 30,597,260 | Merging a dataframe with a Series | <p>A co-worker of mine is trying to combine a matrix and a Series and is trying to see if there is a native pandas way to do so instead of using a loop.</p>
<p>Example if I had a dataframe that consisted of</p>
<pre><code>1, 2, 3
4, 5, 6
7, 8, 9
</code></pre>
<p>and a Series with values</p>
<pre><code>13, 14, 15
</... | <p>You can do this with merge. If you want the full Cartesian product, you can do the following:</p>
<pre><code>import pandas as pd
df = pd.DataFrame([[1,2,3], [4,5,6], [7,8,9]])
df['key'] = 0
ser = pd.DataFrame({'data': [13,14,15], 'key': [0] * 3})
result = pd.merge(df, ser, on = 'key').drop('key', axis = 1)
</code>... | python|pandas|merge|dataframe|series | 2 |
351,677 | 30,298,144 | datetime format change when save to csv file python | <p>I have a dataframe like this:</p>
<pre><code>In [67]:
call_df.head()
Out[67]:
timestamp types
1 2014-06-30 07:00:55 Call_O
2 2014-06-30 07:00:05 Call_O
3 2014-06-30 06:54:55 Call_O
501 2014-06-30 11:24:01 Call_O
</code></pre>
<p>When I saved that dataframe to csv file, the format of datetime is change as w... | <p><a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html#pandas.DataFrame.to_csv" rel="noreferrer"><code>to_csv</code></a> accepts a <code>date_format</code> param, so please try this:</p>
<pre><code>call_df.to_csv('D:/Call.csv', date_format='%Y-%m-%d %H:%M:%S')
</code></pre>
<p>... | python|pandas | 14 |
351,678 | 30,284,478 | Rollaxis error from numpy cross product in Python | <p>I have been trying to determine the source of my error for this simple script which takes a numpy.array as input and produces a new lattice from the dataset</p>
<pre><code>def reciprocalLat(lattice):
for i,a in enumerate(lattice):
print a
b[i]=numpy.cross(a[(i+1)%3],a[(i+2)%3],axis=0)
#/numpy.dot(a[... | <p>With the newer version of <code>np.cross</code> (which uses rollaxis instead of swap), I can produce this error with:</p>
<pre><code>In [663]: np_cross.cross(lat[0,0],lat[0,1],axis=0)
---------------------------------------------------------------------------
ValueError Traceback (mos... | python|numpy|cross-product|vectormath | 2 |
351,679 | 30,400,740 | matlab read h5 file produced with pandas | <p>I have a csv file and I have transformed it in an h5 file with pandas:</p>
<pre><code>data = pd.read_csv('file.csv')
data.to_hdf('file.h5', 'table')
</code></pre>
<p>Now I would like to read it with matlab. </p>
<p>How can I do that?</p>
<p>I have tried </p>
<pre><code>data = h5read('file.h5','/g4/lat');
</code... | <p>You need to export with <code>format='table'</code>, see docs <a href="http://pandas.pydata.org/pandas-docs/stable/io.html#external-compatibility" rel="nofollow">here</a>.</p>
<p>This is can be read by various <code>R</code> packages and should be ok in matlab, as this is plain vanilla <code>HDF5</code>, which some... | matlab|pandas|hdf | 1 |
351,680 | 30,424,161 | Write filenames to csv with Pandas and Regex | <p>How do I write a list of filenames to a column in a csv file, using Pandas? I also want to Regex to keep only a part of the filename.</p>
<p>With the csv module, I have done like this:</p>
<pre><code>import os
import re
import csv
with open("file.csv","w") as write_csv:
fieldnames = ["col1", "col2"]
wr_he... | <p>Pandas dataframe can take a list as a constructor. The real question is where do you do the regex work. Here, I use the pandas string method <code>extract</code> which returns the captured group for each row.</p>
<pre><code>pd.Series(os.listdir(directory)).str.extract(regex).to_csv(filename)
</code></pre>
<p>See <... | python|regex|python-2.7|pandas | 2 |
351,681 | 26,584,465 | Pandas Dataframe index from nested values in dictionary | <p>I am creating a pandas dataframe from historical weather data downloaded from weather underground.</p>
<pre><code>import json
import requests
import pandas as pd
import numpy as np
import datetime
from dateutil.parser import parse
address = "http://api.wunderground.com/api/7036740167876b59/history_20060405/q/CA/San... | <p>Since the <em>dtype</em> held in <code>pretty</code> is just object, you can simply grab them to a list and get indexed. Not sure if this is what you want:</p>
<pre><code># by the way, `r.json` should be without ()`
wu_data = r.json
df = pd.DataFrame.from_dict(wu_data["history"]["observations"])
# just index using... | python|dictionary|pandas | 1 |
351,682 | 26,907,305 | Pandas DataFrame insert column based on values in lists | <p>This may be a simple question. But I have wasted lot of time without figuring out what is happening here. I want to categorize HTTP requests in a web log file based on the resource extensions. Following is what I have tried.</p>
<pre><code>imgstr = ['.png','.gif','.jpeg','.jpg']
docstr = [ '.pdf','.ppt','.doc'... | <p>I'd probably use regular expressions for this.</p>
<pre><code>import pandas as pd
import re
def categoriser(x):
if re.search('(.png|.gif|.jpeg|.jpg)', x):
return 'A'
elif re.search('(.pdf|.ppt|.doc)', x):
return 'B'
elif 'favicon.ico'in x:
return 'C'
elif 'robots.txt'in x:
return 'D'
elif 'GET / H... | python|pandas|dataframe | 0 |
351,683 | 26,600,759 | extra numbers showing up on my axes when i do multiple subplots in matplotlib | <p>hi folks first python question here!</p>
<p>so big picture, i have a panel with 4 dataframes in it. i wanted a function that just plots the panel in 4 subplots, with a tiny bit of data processing on it..</p>
<p>When i try to set up subplots i get weird numbers on both axes of my subplots 2 and higher. I dont know ... | <p>The problem is that your <code>plt.subplots(2,2)</code> call adds four AxesSubplot objects to the figure and the <code>fig.add_subplot()</code> calls each add one more*. You're plotting data to this second set of axes, which is why the rogue tick labels span zero to one. That's the default graph when you don't plot... | python|matplotlib|pandas | 2 |
351,684 | 26,596,101 | Converting long integers to strings in pandas (to avoid scientific notation) | <p>I want the following records (currently displaying as 3.200000e+18 but actually (hopefully) each a different long integer), created using <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.excel.read_excel.html" rel="noreferrer">pd.read_excel()</a>, to be interpreted differently:</p>
<pre><cod... | <p>Convert your column values with <code>NaN</code> into 0 then typcast that column as integer to do so.</p>
<pre><code>df[['class_parent_ref']] = df[['class_parent_ref']].fillna(value = 0)
df['class_parent_ref'] = df['class_parent_ref'].astype(int)
</code></pre>
<p>Or in reading your file, specify <code>keep_default... | python|pandas|long-integer | 2 |
351,685 | 39,272,470 | Average Time difference in pandas | <p>I am trying to calculate the average time difference, in hours/minutes/seconds, iterating on a field - in my example, for each different ip address.
Moreover, a column containing the count of each ip row.</p>
<p>My dataframe looks like:</p>
<pre><code>date ipAddress
2016-08-08 00:39:00 98.249.24... | <p>See <a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html#applying-different-functions-to-dataframe-columns" rel="noreferrer">applying different functions to dataframe columns</a>:</p>
<pre><code>(df.groupby('ipAddress')
.date
.agg({'count': 'count',
'avg_time_diff': lambda group: group.... | python|datetime|pandas | 6 |
351,686 | 39,302,666 | how to add column by using other column's conditions | <p>I have a dataframe.</p>
<pre><code>df=pd.DataFrame({'month':np.arange(1,8)})
</code></pre>
<p>So,I would like to add column by using 'month' columns</p>
<pre><code>if 'month'=1,2,3 the elements = 'term1'
'month'=4,5 the elements = 'term2'
'month'=6,7 the elements = 'term3'
</code></pre>
<p>I wou... | <p>Use <code>numpy.where</code> and <code>Series.isin()</code> method could be one of the options to do it:</p>
<pre><code>import numpy as np
import pandas as pd
df["term"] = np.where(df.month.isin([1,2,3]), "term1", \
np.where(df.month.isin([4,5]), "term2", "term3"))
df
# month term
#0 1 te... | python|pandas | 1 |
351,687 | 39,131,620 | using a DataFrame with columns as named arguments to str.format() | <p>I have a DataFrame like:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'author':["Melville","Hemingway","Faulkner"],
'title':["Moby Dick","The Sun Also Rises","The Sound and the Fury"],
'subject':["whaling","bullfighting","a messed-up family"]
})
</co... | <pre><code>>>> ["Some guy {author} wrote a book called {title} that uses "
"{subject} as a metaphor for the human condition.".format(**x._asdict())
for x in df.itertuples(index=False)]
['Some guy Melville wrote a book called Moby Dick that uses whaling as a metaphor for the human condition.', 'Some g... | python|pandas|string-formatting | 3 |
351,688 | 39,390,616 | Melding several columns by unique value | <p>I have a dataset with a lot of columns. The data is structured like this:</p>
<pre><code>id Artist 1 Artist 2 Artist 3
1 Red hot
2 Wiz Red hot
3 Red hot Wiz Bronson
4 Bronson Bruce Red hot
5 Wiz Bronson
6 Red Hot
</code></pre>
<p>And I need it to ... | <p>I think you can use:</p>
<pre><code>#if column `id` is not index, first set it to index
df.set_index('id', inplace=True)
#create multiindex from columns
df.columns = df.columns.str.split(expand=True)
print (df)
Artist
1 2 3
id
1 Red hot ... | python|excel|pandas | 0 |
351,689 | 39,262,647 | Tensorflow sum tensor from before and after queue | <p>What would happen with this:
<a href="https://i.stack.imgur.com/jPy6s.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/jPy6s.jpg" alt="enter image description here"></a></p>
<p>What I want is that (out1, out2) to the summation is from the same input.
However, as Queue2 is continuously doing enqueu... | <p>The easiest way to make this work is to enqueue <code>out1</code> into <code>queue2</code> alongside the result of <code>op1(out1)</code>. Here's a rough example of how it would look, using two <a href="https://www.tensorflow.org/versions/r0.10/api_docs/python/io_ops.html#FIFOQueue" rel="nofollow"><code>tf.FIFOQueue... | tensorflow | 1 |
351,690 | 39,104,928 | Pandas, map two dataframes, count based on condition | <p>I have written some code to map the ids of two dataframes and if a condition matches then create a count in a specified column in the existing dataframe, I am looking for a more efficient way of calculating it. </p>
<p><strong>Sample Data</strong></p>
<pre><code>import numpy as np
import pandas as pd
d = {'ID' : p... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.crosstab.html" rel="nofollow"><code>crosstab</code></a> with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.merge.html" rel="nofollow"><code>merge</code></a>:</p>
<pre><code>print (pd.crosstab(df1.ID, df1.Tag)) ... | python|pandas|dataframe | 2 |
351,691 | 39,207,890 | Efficient way to make numpy object arrays intern strings | <p>Consider numpy arrays of the <code>object</code> dtype. I can shove anything I want in there.</p>
<p>A common use case for me is to put strings in them. However, for very large arrays, this <em>may</em> use up a lot of memory, depending on how the array is constructed. For example, if you assign a long string (e.g.... | <p>Here's one way to do it, using a dictionary whose values are equal to its keys:</p>
<pre><code>seen = {}
for idx, string in enumerate(file):
arr[idx] = seen.setdefault(string, string)
</code></pre> | python|numpy | 2 |
351,692 | 39,384,539 | How to read data in chunks in Python dataframe? | <p>I want to read the file f in chunks to a dataframe. Here is part of a code that I used.</p>
<pre><code>for i in range(0, maxline, chunksize):
df = pandas.read_csv(f,sep=',', nrows=chunksize, skiprows=i)
df.to_sql(member, engine, if_exists='append',index= False, index_label=None, chunksize=chunksize)
</code></pre>
... | <p>I think it is better to use the parameter <code>chunksize</code> in <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="noreferrer"><code>read_csv</code></a>. Also, use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html" rel="noreferrer"><code>conc... | python|csv|pandas|dataframe|chunks | 5 |
351,693 | 38,969,267 | python pandas selecting columns from a dataframe via a list of column names | <p>I have a dataframe with a lot of columns in it. Now I want to select only certain columns. I have saved all the names of the columns that I want to select into a Python list and now I want to filter my dataframe according to this list. </p>
<p>I've been trying to do:</p>
<pre><code>df_new = df[[list]]
</code></pre... | <p>You can remove one <code>[]</code>:</p>
<pre><code>df_new = df[list]
</code></pre>
<p>Also better is use other name as <code>list</code>, e.g. <code>L</code>:</p>
<pre><code>df_new = df[L]
</code></pre>
<p>It look like working, I try only simplify it:</p>
<pre><code>L = []
for x in df.columns:
if not "_" i... | python|pandas|dataframe | 40 |
351,694 | 39,093,727 | Tensorflow: Dequeue and then enqueue | <p>I have a queue (called <code>queue_A</code>) and populate 100 elements inside. If I would like to do the following 2 things:</p>
<ol>
<li>Dequeue 1 element from <code>queue_A</code>, do some processing on it and enqueue the result into another queue (<code>queue_B</code>). The enqueuing op is called <code>op_B</cod... | <p>The code in your example has two types of "queue runner":</p>
<ol>
<li>One that runs <code>op_A</code>: it dequeues an element from <code>queue_A</code>, and enqueues it back to <code>queue_B</code>.</li>
<li>Another that runs <code>op_B</code>: it dequeues an element from <code>queue_A</code>, processes it via <co... | tensorflow | 3 |
351,695 | 39,355,717 | Having issue summarising python dataframe to one line per record | <p>I've got a dataframe in the form:</p>
<pre><code>df = pd.DataFrame({'id':['a', 'a', 'a', 'b','b'],'var':[1,2,3,5,9]})
</code></pre>
<p>and I'm trying to reshape it so that there is one line per 'id' and the values 'var' are displayed across in one line, so 'a' would have 1,2,3 ... 'b' would have '5,9'</p>
<p>I've... | <p>You must supply the correct arguments, like:</p>
<pre><code>pd.crosstab(index=df['id'], columns=df['var'])
var 1 2 3 5 9
id
a 1 1 1 0 0
b 0 0 0 1 1
</code></pre> | python|pandas|dataframe|pivot-table | 3 |
351,696 | 38,965,667 | Pandas: take whichever column is not NaN | <p>I am working with a fairly messy data set that has been individual csv files with slightly different names. It would be too onerous to rename columns in the csv file, partly because I am still discovering all the variations, so I am looking to determine, for a set of columns, in a given row, which field is not NaN a... | <p>For a dataframe with an arbitrary number of columns, you can back fill the rows (<code>.bfill(axis=1)</code>) and take the first column (<code>.iloc[:, 0]</code>):</p>
<pre><code>df = pd.DataFrame({
'A': [15, None, None, None, 12],
'B': [None, 11, 99, None, 14],
'C': [10, None, 10, 10, 10]})
df['D'] = ... | python|pandas | 6 |
351,697 | 39,051,676 | Expanding each element in a (2-by-2) matrix to a (3-by-2) block | <p>I want to expand each element in a (2-by-2) matrix to a (3-by-2) block, using Python 3 --- with professional and elegant codes. Since I don't know the python codes, I will just describe the following in maths</p>
<pre><code>X = # X is an 2-by-2 matrix.
1, 2
3, 4
d = (3,2) # d is the s... | <p>If you are okay with using <a href="http://www.numpy.org/" rel="nofollow"><code>NumPy module</code></a> with Python, you can use <a href="http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.kron.html" rel="nofollow"><code>numpy.kron</code></a> -</p>
<pre><code>np.kron(X,np.ones((3,2),dtype=int))
</code... | matlab|python-3.x|numpy|matrix | 3 |
351,698 | 39,116,088 | TypeError in Countvectorizer scikit-learn: Expected string or buffer | <p>I am trying to solve a classification problem. when I feed the text to CountVectorizer it gives error: </p>
<blockquote>
<p>expected string or buffer. </p>
</blockquote>
<p>Is anything wrong with my dataset as it contains message mixture of number and word even special character is also in message.</p>
<p>Sampl... | <p>You need convert column <code>message</code> to <code>string</code> by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html" rel="nofollow"><code>astype</code></a>, because in data are some numeric values:</p>
<pre><code>df = pd.read_excel('training_data.xlsx')
df['message'] = df... | python-2.7|pandas|dataframe|scikit-learn|text-classification | 1 |
351,699 | 39,028,903 | Transforming column dataframe pandas into sequence | <p>I have data and convert into dataframe</p>
<pre><code>d = [
(1,70399,0.988375133622),
(1,33919,0.981573492596),
(1,62461,0.981426807114),
(579,1,0.983018778374),
(745,1,0.995580488899),
(834,1,0.980942505189)
]
df = pd.DataFrame(d, columns=['source', 'target', 'weight'])
>>> df
source tar... | <p>You can use:</p>
<pre><code>#remember original values
source_old = df.source.copy()
df.source = (df.source.diff() != 0).cumsum() - 1
#series for maping
ser = pd.Series(df.source.values, index=source_old).drop_duplicates()
print (ser)
source
1 0
579 1
745 2
834 3
dtype: int32
#map where values exist... | python|pandas | 0 |
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